Gdf-15 as a diagnostic marker to predict the clinical outcome of a treatment with immune checkpoint blockers

ABSTRACT

The present invention relates to methods for predicting the probability of a treatment response of a human cancer patient to an immune checkpoint blocker treatment e.g. with anti PD-1, and to methods for predicting the probability of survival of a human cancer patient following an immune checkpoint blocker treatment, and to apparatuses and kits which can be used in these methods.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No. 15/765,174, filed Mar. 30, 2018, which is a 35 U.S.C. § 371 filing of International Patent Application No. No. PCT/EP2016/073519, filed Sep. 30, 2016, which claims priority to Great Britain Application No. 1517527.6, filed Oct. 2, 2015, and Great Britain Application No. 1607800.8, filed Apr. 29, 2016. Each of the aforementioned applications is hereby incorporated by reference in its entirety.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Mar. 30, 2018, is named 404224_CATW_003USD1_188425_Sequence_Listing, and is 13,638 bytes in size.

FIELD OF THE INVENTION

The present invention relates to methods for predicting the probability of a treatment response of a human cancer patient to an immune checkpoint blocker treatment, and to methods for predicting the probability of survival of a human cancer patient following an immune checkpoint blocker treatment, and to apparatuses and kits which can be used in these methods.

BACKGROUND

To date, many cancers are still areas of unmet medical needs. Additionally, for several cancers, subsets of patients show a response to cancer therapies, while other subsets of patients do not respond. In many cases, the factors determining whether or not the patients will respond to a particular cancer treatment are still unknown.

Many types of cancer are known to express growth factors, including factors such as VEGF, PDGF, TGF-p and GDF-15. GDF-15, growth and differentiation factor-15, is a divergent member of the TGF-p superfamily. It is a protein which is intracellularly expressed as a precursor, subsequently processed and eventually becomes secreted from the cell into the environment. Both the active, fully processed (mature) form and the precursor of GDF-15 can be found outside cells. The precursor covalently binds via its COOH-terminal amino acid sequence to the extracellular matrix (Bauskin A R et al., Cancer Research 2005) and thus resides on the exterior of a cell. The active, fully processed (mature) form of GDF-15 is soluble and is found in blood sera. Thus, the processed form of GDF-15 may potentially act on any target cell within the body that is connected to the blood circulation, provided that the potential target cell expresses a receptor for the soluble GDF-15 ligand.

During pregnancy, GDF-15 is found under physiological conditions in the placenta. However, many malignant cancers (especially aggressive brain cancers, melanoma, lung cancer, gastrointestinal tumors, colon cancer, pancreatic cancer, prostate cancer and breast cancer (Mimeault M and Batra S K, J. Cell Physiol 2010)) exhibit increased GDF-15 levels in the tumor as well as in blood serum. Likewise, correlations have been described between high GDF-15 expression and chemoresistance (Huang C Y et al., Clin. Cancer Res. 2009) and between high GDF-15 expression and poor prognosis, respectively (Brown D A et al., Clin. Cancer Res. 2009). Wallentin L et al. (PLoS One. 2013 Dec. 2; 8(12):e78797.) used GDF-15 to prognosticate cardiovascular and cancer morbidity and mortality in men.

GDF-15 is expressed in gliomas of different WHO grades as assessed by immunohistochemistry (Roth et al., Clin. Cancer Res. 2010). Further, Roth et al. stably expressed short hairpin RNA-expressing DNA constructs targeting endogenous GDF-15 or control constructs in SMA560 glioma cells. When using these pre-established stable cell lines, they observed that tumor formation in mice bearing GDF-15 knockdown SMA560 cells was delayed compared to mice bearing control constructs.

Patent applications WO 2005/099746 and WO 2009/021293 relate to an anti-human-GDF-15 antibody (Mab26) capable of antagonizing effects of human GDF-15 (hGDF-15) on tumor-induced weight loss in vivo in mice. Similarly, Johnen H et al. (Nature Medicine, 2007) reported effects of an anti-human-GDF-15 monoclonal antibody on cancer-induced anorexia and weight loss but did not observe any effects of the anti-human-GDF-15 antibody on the size of the tumor formed by the cancer.

WO 2014/049087 and PCT/EP2015/056654 relate to monoclonal antibodies to hGDF-15 and medical uses thereof.

A recently developed approach to cancer therapy is the use of immune checkpoint blockers such as inhibitors of human PD-1 and inhibitors of human PD-L1. A rationale behind the use of these immune checkpoint blockers is that by blocking immune checkpoints which prevent the immune system from targeting cancer antigens and the respective cancer cells, an immune response to the cancer may become more effective. While immune checkpoint blockers as well as particular combinations of immune checkpoint blockers have been shown to improve patient survival in melanoma patients (Cully M, “Combinations with checkpoint inhibitors at wavefront of cancer immunotherapy.”, Nat Rev Drug Discov. 2015 June; 14(6):374-5.), not all melanoma patients exhibited a complete response, and results for many other cancers are yet to be disclosed, still there are reasons (like the mutational burden) which suggest that results in other indications will be less favorable. The current landmark is the KEYNOTE-006 (ClinicalTrials.gov number, NCT01866319) trial with a response rate just below 34%. Robert et al. Pembrolizumab versus Ipilimumab in Advanced Melanoma. N Engl J Med 2015; 372:2521-2532.

The current knowledge of prognostic factors predicting whether or not the cancer patients will respond to a treatment with immune checkpoint blockers is still quite limited. One particular factor which has been shown to correlate with an improved objective response, durable clinical benefit, and progression-free survival in non-small cell lung cancer patients treated with a PD-1 inhibitor is a higher nonsynonymous mutation burden in the tumors (Rizvi N A et al.: “Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer.” Science 2015 Apr. 3; 348(6230):124-8.). However, in order to determine such mutation burden on the level of the whole exome, laborious whole-exome sequencing is necessary.

Another specific prognostic factor correlating with an objective response to anti-PD-1 therapy in several tumor entities including melanoma and non-small cell lung cancer is PD-L1 expression of tumor cells (Taube et al., “Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy.”, Clin Cancer Res. 2014 Oct. 1; 20(19):5064-74.). In contrast, however, a meta-analysis found that for patient populations which included non-small cell lung cancer patients that had not been treated with anti-PD-1 therapy, high PD-L1 expression was correlated with poor prognosis rather than with a favorable prognosis (Wang A et al.: “The prognostic value of PD-L1 expression for non-small cell lung cancer patients: a meta-analysis.” Eur J Surg Oncol. 2015 April; 41(4):450-6.). Moreover, responses to anti-PD-1 treatment have also been observed in patients whose excised tumor sections showed no discernable staining for PD-L1 expression. Furthermore, a recent study in advanced renal cell carcinoma found that there is no significant correlation between the expression of PD-L1 and the response to the anti-PD-1 antibody nivolumab in the patients (Motzer R J et al., Nivolumab versus Everolimus in Advanced Renal-Cell Carcinoma. N Engl J Med. 2015 Sep. 25). Additionally, the detection of PD-L1 expression requires tissue samples, which are not always available. Hence the detection of PD-L1 expression has many disadvantages with respect to its use as a potential diagnostic marker.

From these opposing results concerning the predictive value of high PD-L1 expression in anti-PD-1-treated patient groups compared to other patient groups, it is apparent that treatment with immune checkpoint blockers is a specific form of cancer treatment which is distinct from other cancer therapies, and which follows a different set of prognostic factors.

One of the reasons for these differences between conventional cancer therapies and the therapy with immune checkpoint blockers is an activation of the immune system by the immune checkpoint blockers, which is a mechanism that is not commonly observed in other cancer therapies.

Additionally, the above-mentioned methods for diagnosis are disadvantageous due to the fact that they either require whole-exome sequencing or an existing tumor sample and its analysis. Searching for other markers, K K Tsai et al. found only weak predictors among which the pattern of metastasis (presence of lung metastasis and absence of liver metastasis), no prior ipilimumab (i.e. early treatment with anti PD-1) and normal LDH levels were the best (K K Tsai et al., JCO 33, 2015 (suppl. abstr. 9031). However, while the overall response rate was 40%, the best subgroup (lung metastasis and no liver metastasis) showed a response rate of 62.2%. Among patients with low or normal LDH, the response rate was 52.2%.

For these reasons, there is a need in the art to identify prognostic factors which can be used to predict the probability of a clinical outcome of these new treatments with immune checkpoint blockers. Furthermore, prognostic factors are needed which allow to predict a probability of a clinical outcome of such treatments in a less laborious and easier way.

DESCRIPTION OF THE INVENTION The present invention meets the above needs and solves the above problems in the art by providing the embodiments described below:

In particular, in an effort to identify factors which can be used to predict a response to treatments with immune checkpoint blockers, the present inventors have surprisingly found that the probability of a positive clinical outcome to a treatment with immune checkpoint blockers significantly decreases with increasing hGDF-15 levels in the patient sera and vice versa. Accordingly, the probability of a positive clinical outcome of a treatment with immune checkpoint blockers inversely correlates with hGDF-15 levels. This clinical outcome can, for instance, be a response to the treatment with immune checkpoint blockers or patient survival following the treatment with immune checkpoint blockers.

For instance, if hGDF-15 serum levels in melanoma patients are increased by 1 ng/ml, the probability of a response to a treatment with an immune checkpoint blocker decreases by about 60%. Conversely, if hGDF-15 serum levels in melanoma patients are decreased by 1 ng/ml, the probability of a response to a treatment with an immune checkpoint blocker increases by about 60%. Similarly, if hGDF-15 serum levels are increased by 1 ng/ml, the patients' probability to die increases by a factor of 1.27.

hGDF-15 expression is not limited to melanoma but also present in numerous other solid cancers. Likewise, solid tumors other than melanoma can also be treated with immune checkpoint blockers. Thus, according to the invention, levels of hGDF-15 in blood samples from patients can advantageously be used to predict the probability of a positive clinical outcome of the patients following a treatment with immune checkpoint blockers not only in melanoma, but in all of the solid cancers referred to herein.

Furthermore, the methods of the invention are also advantageous because they do not require whole-exome sequencing or an existing tumor sample (which may not always be available) but can be based on a simple analysis of a blood sample.

Thus, the present invention provides improved means to predict the clinical outcome of treatments with immune checkpoint blockers by providing the preferred embodiments described below:

-   1. A method for predicting the probability of a treatment response     of a human cancer patient to an immune checkpoint blocker treatment,     wherein the method comprises the steps of:     -   a) determining the level of hGDF-15 in a human blood sample         obtained from said patient; and     -   b) predicting said probability of a treatment response based on         the determined level of hGDF-15 in said human blood sample;         wherein a decreased level of hGDF-15 in said human blood sample         indicates an increased probability of a treatment response, and         wherein the cancer is a solid cancer. -   2. A method for predicting the probability of survival of a human     cancer patient following an immune checkpoint blocker treatment,     wherein the method comprises the steps of:     -   a) determining the level of hGDF-15 in a human blood sample         obtained from said patient; and     -   b) predicting said probability of survival based on the         determined level of hGDF-15 in said human blood sample; wherein         a decreased level of hGDF-15 in said human blood sample         indicates an increased probability of survival, and wherein the         cancer is a solid cancer. -   3. The method according to item 1 or 2, wherein step b) comprises     comparing said level of hGDF-15 determined in step a) with a hGDF-15     threshold level, wherein said probability is predicted based on the     comparison of said level of hGDF-15 determined in step a) with said     hGDF-15 threshold level; and wherein a level of hGDF-15 in said     human blood sample which is decreased compared to said hGDF-15     threshold level indicates that said probability is increased     compared to a probability at or above said hGDF-15 threshold level. -   4. The method according to item 1, 2 or 3, wherein the human blood     sample is a human serum sample. -   5. The method according to item 4, wherein the hGDF-15 threshold     level is a hGDF-15 level selected from the range of between 1.2     ng/ml and 8.0 ng/ml, or wherein the hGDF-15 threshold level is a     hGDF-15 level selected from the range of between 1.5 ng/ml and 7.0     ng/ml, or wherein the hGDF-15 hGDF-15 threshold level is a hGDF-15     level selected from the range of between 2.0 ng/ml and 6.0 ng/ml, or     wherein the hGDF-15 threshold level is a hGDF-15 level selected from     the range of between 2.5 ng/ml and 5.0 ng/ml, or wherein the hGDF-15     threshold level is a hGDF-15 level selected from the range of     between 3.0 ng/ml and 4.0 ng/ml. -   6. The method according to any one of the preceding items, wherein a     decreased level of hGDF-15 in said human blood sample compared to     said hGDF-15 threshold level indicates that said increased     probability is a probability of higher than 5%, higher than 10%,     higher than 20%, higher than 30%, higher than 40%, higher than 50%,     higher than 60%, higher than 70%, higher than 80%, or higher than     90%. -   7. The method according to any one of the preceding items, wherein     the solid cancer is selected from the group consisting of melanoma,     colorectal cancer, prostate cancer, head and neck cancer, urothelial     cancer, stomach cancer, pancreatic cancer, liver cancer, testis     cancer, ovarian cancer, endometrial cancer, cervical cancer, brain     cancer, breast cancer, gastric cancer, renal cell carcinoma, Ewing's     sarcoma, non-small cell lung cancer and small cell lung cancer,     wherein the cancer is preferably selected from the group consisting     of melanoma, colorectal cancer, prostate cancer, head and neck     cancer, urothelial cancer, stomach cancer, pancreatic cancer, liver     cancer, testis cancer, ovarian cancer, endometrial cancer and     cervical cancer, and wherein the cancer is more preferably selected     from the group consisting of melanoma, colorectal cancer, prostate     cancer, head and neck cancer, urothelial cancer and stomach cancer. -   8. The method according to any one of the preceding items, wherein     the cancer is selected from the group consisting of melanoma, oral     squamous cell carcinoma, colorectal cancer and prostate cancer. -   9. The method according to any one of the preceding items, wherein     the cancer is melanoma. -   10. The method according to item 9, wherein the hGDF-15 threshold     level is a hGDF-15 level selected from the range of between 3.0     ng/ml and 4.0 ng/ml, wherein the hGDF-15 threshold level is     preferably a hGDF-15 level selected from the range of between 3.2     ng/ml and 3.7 ng/ml, and wherein the hGDF-15 threshold level is most     preferably a hGDF-15 level of 3.4 ng/ml. -   11. The method according to any of the preceding items, wherein     step a) comprises determining the level of hGDF-15 by using one or     more antibodies capable of binding to hGDF-15 or an antigen-binding     portion thereof. -   12. The method according to item 11, wherein the one or more     antibodies capable of binding to hGDF-15 or the antigen-binding     portion thereof form a complex with hGDF-15. -   13. The method according to item 11 or 12, wherein the one or more     antibodies comprise at least one polyclonal antibody. -   14. The method according to item 11, 12 or 13, wherein the one or     more antibodies or the antigen-binding portion comprise at least one     monoclonal antibody or an antigen-binding portion thereof. -   15. The method according to item 14, wherein the binding is binding     to a conformational or discontinuous epitope on hGDF-15, and wherein     the conformational or discontinuous epitope is comprised by the     amino acid sequences of SEQ ID No: 25 and SEQ ID No: 26. -   16. The method according to item 14 or 15, wherein the antibody or     antigen-binding portion thereof comprises a heavy chain variable     domain which comprises a CDR1 region comprising the amino acid     sequence of SEQ ID NO: 3, a CDR2 region comprising the amino acid     sequence of SEQ ID NO: 4 and a CDR3 region comprising the amino acid     sequence of SEQ ID NO: 5, and wherein the antibody or     antigen-binding portion thereof comprises a light chain variable     domain which comprises a CDR1 region comprising the amino acid     sequence of SEQ ID NO: 6, a CDR2 region comprising the amino acid     sequence ser-ala-ser and a CDR3 region comprising the amino acid     sequence of SEQ ID NO: 7. -   17. The method according to any of the preceding items, wherein the     immune checkpoint blocker is selected from one or more of the     following group consisting of:     -   i) an inhibitor of human PD-1, the inhibitor preferably being a         monoclonal antibody capable of binding to human PD-1, or an         antigen-binding portion thereof; and     -   ii) an inhibitor of human PD-L1, the inhibitor preferably being         a monoclonal antibody capable of binding to human PD-L1, or an         antigen-binding portion thereof. -   18. The method according to item 17, wherein the immune checkpoint     blocker comprises a monoclonal antibody capable of binding to human     PD-1, or an antigen-binding portion thereof. -   19. The method according to item 17 or 18, wherein the immune     checkpoint blocker comprises a monoclonal antibody capable of     binding to human PD-L1, or an antigen-binding portion thereof. -   20. The method according to any one of the preceding items, wherein     the human blood sample obtained from the human patient is a sample     from a patient who has received said immune checkpoint blocker. -   21. The method according to item 20, wherein the human blood sample     obtained from the human patient contains the immune checkpoint     blocker and/or biological metabolites thereof. -   22. The method according to any one of items 1-19, wherein the human     blood sample obtained from the human patient is a sample from a     patient who has not received any immune checkpoint blocker. -   23. The method according to any one of the preceding items, wherein     the method is an in vitro method. -   24. The method according to any one of the preceding items, wherein     in step a), the level of hGDF-15 in the human blood sample is     determined by an enzyme linked immunosorbent assay. -   25. The method according to any one of items 1-23, wherein in step     a), the level of hGDF-15 in the human blood sample is determined by     an electrochemiluminescence assay. -   26. The method according to item 25, wherein the     electrochemiluminescence assay is a sandwich detection method     comprising a step of forming a detection complex between     -   (A) streptavidin-coated beads or streptavidin-coated         paramagnetic nanoparticles;     -   (B) a biotinylated first antibody or antigen-binding portion         thereof capable of binding to hGDF-15;     -   (C) hGDF-15 from the sample; and     -   (D) a ruthenium complex-labelled second antibody or         antigen-binding portion thereof capable of binding to hGDF-15;     -   wherein said detection complex has the structure         (A)-(B)-(C)-(D), and wherein the biotinylated first antibody or         antigen-binding portion thereof binds to a first hGDF-15 epitope         and the ruthenium complex-labelled second antibody or         antigen-binding portion thereof binds to a second hGDF-15         epitope which is different from said first hGDF-15 epitope,     -   wherein the method further comprises a step of detecting the         detection complex by measuring electrochemiluminescence,     -   and wherein the level of hGDF-15 in the human blood sample is         determined based on the electrochemiluminescence measurement. -   27. The method according to any one of items 1 and 3-26, wherein the     human blood sample is a human serum sample, and wherein said     probability of a treatment response is predicted using an odds ratio     of 0.389 for serum levels of hGDF-15 in ng/ml as a continuous     predictor with a 95% confidence interval of from 0.159 to 0.698. -   28. The method according to any one of items 1 and 3-27, wherein the     treatment response is a response according to the RECIST criteria,     version 1.1. -   29. The method according to any one of items 2-26, wherein the     probability of survival is predicted using a Hazard ratio with     overall survival as outcome variable and GDF-15 as continuous     predictor, and wherein it is predicted that per 1 ng/ml increase in     GDF-15 serum levels, the risk to die increases by a factor of 1.27     with a 95% confidence interval of from 1.10 to 1.47. -   30. The method according to any one of items 11 to 29, wherein in     step a), the level of hGDF-15 is determined by capturing hGDF-15     with an antibody or antigen-binding fragment thereof according to     any one of items 14 to 16 and by detecting hGDF-15 with a polyclonal     antibody, or by detecting hGDF-15 with a monoclonal antibody or     antigen-binding fragment thereof which binds to a different epitope     than the antibody which captures hGDF-15. -   31. An apparatus configured to perform the method according to any     one of items 1-30. -   32. The apparatus according to item 28, wherein the apparatus is an     electrochemiluminescence analyzer configured to perform the method     according to item 25 or item 26. -   33. A detection kit comprising:     -   (i) streptavidin-coated beads;     -   (ii) a biotinylated first antibody or antigen-binding portion         thereof capable of binding to hGDF-15;     -   (iii) recombinant hGDF-15;     -   (iv) a ruthenium complex-labelled second antibody or         antigen-binding portion thereof capable of binding to hGDF-15;         and optionally     -   (v) instructions for use, preferably instructions for use in a         method according to items 1-30.     -   wherein the biotinylated first antibody or antigen-binding         portion thereof is capable of binding to a first hGDF-15 epitope         and the ruthenium complex-labelled second antibody or         antigen-binding portion thereof is capable of binding to a         second hGDF-15 epitope which is different from said first         hGDF-15 epitope. -   34. The detection kit according to item 33, wherein one of the first     antibody or antigen-binding portion thereof capable of binding to     hGDF-15 and second antibody or antigen-binding portion thereof     capable of binding to hGDF-15 is an antibody or antigen-binding     portion thereof according to any one of items 15 to 16. -   35. Use of a kit of any one of items 33 to 34 in an in vitro method     for the prediction of a response of a human cancer patient to an     immune checkpoint blocker, wherein the cancer is a solid cancer. -   36. Use of a kit of any one of items 33 to 34 in an in vitro method     for the prediction of the probability of survival of a human cancer     patient following an immune checkpoint blocker treatment, wherein     the cancer is a solid cancer. -   37. The use of any one of items 33 to 36, wherein the immune     checkpoint blocker is as defined in any one of items 17 to 19.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1: This Figure shows the GDF-15 serum levels for responders and non-responders to the treatment regimen.

FIG. 2: This Figure shows the numbers of responders and non-responders in the patient groups having hGDF-15 serum levels of <1.8 ng/ml, 1.8-4.2 ng/ml, and >4.2 ng/ml, respectively.

FIG. 3: Probability of response to treatment (responder 1) as predicted by the Generalized Linear Model using GDF-15 as continuous predictor. Circles show the data, the curve shows the model. The vertical line indicates the GDF-15 concentration where the probability of treatment response is 0.5.

FIG. 4: Kaplan-Meier curves for survival in the three groups defined by the GDF-15 serum level (<1.8, 1.8-4.2, >4.2 ng/ml).

FIG. 5: FIG. 5A: Probability of response to treatment (responder 1) as predicted by the Generalized Linear Model model using LDH as continuous predictor. Circles show the data, the curve shows the model. The vertical line indicates the LDH concentration where the probability of treatment response is 0.5. The patient cohort was identical. However, reliable determination of LDH levels failed in four patients due to hemolysis. FIG. 5B: Graphical representation of responders and non-responders and their respective hGDF-15 and LDH levels. When cut-off values are selected to cover all responders, testing based on GDF-15 allows for identification of 6 (out of 9) non-responders whereas analyses based on LDH levels can only discriminate 4 (out of 9) non-responders. For LDH testing, 4 hemolytic samples had to be excluded which causes loss of data.

FIG. 6: This Figure shows exemplary tissue sections from melanoma brain metastases having no (upper panel) or high (lower panel) GDF-15 immunoreactivity, which were stained by immunohistochemistry for GDF-15 and for the T-cell marker proteins CD3 and CD8, respectively, as indicated in the Figure. CD3 and CD8-positive cells are indicated by arrows in the high GDF-15 samples. The CD3 and CD8 stainings were made from the same area of serial sections (however not from the identical section).

FIG. 7: This Figure shows a plot of the percentage of CD3⁺ cells against the GDF-15 score across different melanoma brain metastases (7A) and a plot of the percentage of CD8⁺ cells against the GDF-15 score across different melanoma brain metastases (7B).

FIG. 8: This Figure shows a plot of the GDF-15 score against the percentage of CD8⁺ and CD3⁺ T cells, respectively, in brain metastases from different tumor entities (melanoma, CRC, RCC, NSCLC and SCLC).

FIG. 9: FIG. 9A shows the number of rolling T cells per field of view per second. Data were obtained from channel #3 (“GDF-15”) and channel #2 (“control”). FIG. 9B shows the rolling speed of the T cells (measured in pixels per 0.2 seconds). Data were obtained from channel #3 (“GDF-15”) and channel #2 (“control”). FIG. 9C shows the number of adhering cells per field of view. Data were obtained from channel #3 (“GDF-15”) and channel #2 (“control”). FIG. 9D shows the number of adhering cells per field of view. Data were obtained from channel #3 (“GDF-15”) and channel #2 (“control”).

FIG. 10: FIG. 10 shows the number of rolling T cells per field of view per second. Data were obtained from channel #1 (control T cells on unstimulated HUVEC as “neg. control”), channel #2 (control T cells on stimulated HUVEC as “pos. control”), channel #3 (“GDF-15”) channel #4 (“UACC 257”: T cells cultured in the supernatant of UACC 257 melanoma cells containing secreted GDF-15) and channel #5 (“UACC257+ anti-hGDF-15”: T cells cultured in the supernatant of UACC 257 melanoma cells depleted from secreted GDF-15 with an anti-hGDF-15 antibody B1-23 as an hGDF-15 inhibitor).

FIG. 11: Cumulative survival in patient groups having GDF-15 levels of <1.5 ng/ml and ≥1.5 ng/ml, respectively.

FIG. 12: Cumulative survival in patient groups having high GDF-15 levels (i.e. the 50 patients with the highest GDF-15 levels) and low GDF-15 levels (i.e. the 49 patients with the lowest GDF-15 levels), respectively (median split of the total study cohort).

FIG. 13: hGDF-15 Serum Levels do not Significantly Correlate with the Mutational Burden of the Tumors. hGDF-15 mRNA levels in samples from cancer patients were plotted against the number of somatic mutations which were identified in the cancers. The somatic mutations were determined by use of exome sequencing. The data were analyzed by using the UZH webtool from the University Hospital Zurich (Cheng P F et al.: Data mining The Cancer Genome Atlas in the era of precision cancer medicine. Swiss Med Wkly. 2015 Sep. 16; 145:w14183.). FIG. 13A shows a plot for cancer patient data obtained from the Cancer Genome Atlas (TGCA) considering only patients with high-grade malignant melanoma (the Cancer Genome Atlas is described in the reference of Cheng P F et al.: Data mining The Cancer Genome Atlas in the era of precision cancer medicine. Swiss Med Wkly. 2015 Sep. 16; 145:w14183.). GDF-15 expression was evaluated by normalization using the RSEM (“RNA Seq by expectation maximization”) software package (Li B and Dewey CN: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011 Aug. 4; 12:323. doi: 10.1186/1471-2105-12-323.). FIG. 13B shows a plot for cancer patient data from 40 additional metastatic malignant melanoma patients from the University Hospital Zurich, which were separately analyzed.

FIG. 14: Immunocytochemistry pictures for CD8a in mice harboring wild-type tumors or tumors overexpressing transgenic (tg) hGDF15 are shown. Tissue sections were stained with anti-CD8a (1:100 dilution; 4SM15 antibody purchased from eBioscience).

DETAILED DESCRIPTION OF THE INVENTION Definitions and General Techniques

Unless otherwise defined below, the terms used in the present invention shall be understood in accordance with their common meaning known to the person skilled in the art.

The term “antibody” as used herein refers to any functional antibody that is capable of specific binding to the antigen of interest, as generally outlined in chapter 7 of Paul, W. E. (Ed.).: Fundamental Immunology 2nd Ed. Raven Press, Ltd., New York 1989, which is incorporated herein by reference. Without particular limitation, the term “antibody” encompasses antibodies from any appropriate source species, including chicken and mammalian such as mouse, goat, non-human primate and human. Preferably, the antibody is a humanized antibody. The antibody is preferably a monoclonal antibody which can be prepared by methods well-known in the art. The term “antibody” encompasses an IgG-1, -2, -3, or -4, IgE, IgA, IgM, or IgD isotype antibody. The term “antibody” encompasses monomeric antibodies (such as IgD, IgE, IgG) or oligomeric antibodies (such as IgA or IgM). The term “antibody” also encompasses—without particular limitations—isolated antibodies and modified antibodies such as genetically engineered antibodies, e.g. chimeric antibodies.

The nomenclature of the domains of antibodies follows the terms as known in the art. Each monomer of an antibody comprises two heavy chains and two light chains, as generally known in the art. Of these, each heavy and light chain comprises a variable domain (termed V_(H) for the heavy chain and V_(L) for the light chain) which is important for antigen binding. These heavy and light chain variable domains comprise (in an N-terminal to C-terminal order) the regions FR1, CDR1, FR2, CDR2, FR3, CDR3, and FR4 (FR, framework region; CDR, complementarity determining region which is also known as hypervariable region). The identification and assignment of the above-mentioned antibody regions within the antibody sequence is generally in accordance with Kabat et al. (Sequences of proteins of immunological interest, U.S. Dept. of Health and Human Services, Public Health Service, National Institutes of Health, Bethesda, Md. 1983), or Chothia et al. (Conformations of immunoglobulin hypervariable regions. Nature. 1989 Dec. 21-28; 342(6252):877-83.), or may be performed by using the IMGTN-QUEST software described in Giudicelli et al. (IMGTN-QUEST, an integrated software program for immunoglobulin and T cell receptor V-J and V-D-J rearrangement analysis. Nucleic Acids Res. 2004 Jul. 1; 32(Web Server issue):W435-40.), which is incorporated herein by reference. Preferably, the antibody regions indicated above are identified and assigned by using the IMGT/V-QUEST software.

A “monoclonal antibody” is an antibody from an essentially homogenous population of antibodies, wherein the antibodies are substantially identical in sequence (i.e. identical except for minor fraction of antibodies containing naturally occurring sequence modifications such as amino acid modifications at their N- and C-termini). Unlike polyclonal antibodies which contain a mixture of different antibodies directed to either a single epitope or to numerous different epitopes, monoclonal antibodies are directed to the same epitope and are therefore highly specific. The term “monoclonal antibody” includes (but is not limited to) antibodies which are obtained from a monoclonal cell population derived from a single cell clone, as for instance the antibodies generated by the hybridoma method described in Kohler and Milstein (Nature, 1975 Aug. 7; 256(5517):495-7) or Harlow and Lane (“Antibodies: A Laboratory Manual” Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York 1988). A monoclonal antibody may also be obtained from other suitable methods, including phage display techniques such as those described in Clackson et al. (Nature. 1991 Aug. 15; 352(6336):624-8) or Marks et al. (J Mol Biol. 1991 Dec. 5; 222(3):581-97). A monoclonal antibody may be an antibody that has been optimized for antigen-binding properties such as decreased Kd values, optimized association and dissociation kinetics by methods known in the art. For instance, Kd values may be optimized by display methods including phage display, resulting in affinity-matured monoclonal antibodies. The term “monoclonal antibody” is not limited to antibody sequences from particular species of origin or from one single species of origin. Thus, the meaning of the term “monoclonal antibody” encompasses chimeric monoclonal antibodies such as humanized monoclonal antibodies.

“Humanized antibodies” are antibodies which contain human sequences and a minor portion of non-human sequences which confer binding specificity to an antigen of interest (e.g. human GDF-15). Typically, humanized antibodies are generated by replacing hypervariable region sequences from a human acceptor antibody by hypervariable region sequences from a non-human donor antibody (e.g. a mouse, rabbit, rat donor antibody) that binds to an antigen of interest (e.g. human GDF-15). In some cases, framework region sequences of the acceptor antibody may also be replaced by the corresponding sequences of the donor antibody. In addition to the sequences derived from the donor and acceptor antibodies, a “humanized antibody” may either contain other (additional or substitute) residues or sequences or not. Such other residues or sequences may serve to further improve antibody properties such as binding properties (e.g. to decrease Kd values) and/or immunogenic properties (e.g. to decrease antigenicity in humans). Non-limiting examples for methods to generate humanized antibodies are known in the art, e.g. from Riechmann et al. (Nature. 1988 Mar. 24; 332(6162):323-7) or Jones et al. (Nature. 1986 May 29-Jun. 4; 321(6069):522-5).

The term “human antibody” relates to an antibody containing human variable and constant domain sequences. This definition encompasses antibodies having human sequences bearing single amino acid substitutions or modifications which may serve to further improve antibody properties such as binding properties (e.g. to decrease Kd values) and/or immunogenic properties (e.g. to decrease antigenicity in humans). The term “human antibody” excludes humanized antibodies where a portion of non-human sequences confers binding specificity to an antigen of interest.

An “antigen-binding portion” of an antibody as used herein refers to a portion of an antibody that retains the capability of the antibody to specifically bind to the antigen (e.g. hGDF-15, PD-1 or PD-L1). This capability can, for instance, be determined by determining the capability of the antigen-binding portion to compete with the antibody for specific binding to the antigen by methods known in the art. The antigen-binding portion may contain one or more fragments of the antibody. Without particular limitation, the antigen-binding portion can be produced by any suitable method known in the art, including recombinant DNA methods and preparation by chemical or enzymatic fragmentation of antibodies. Antigen-binding portions may be Fab fragments, F(ab′) fragments, F(ab′)₂ fragments, single chain antibodies (scFv), single-domain antibodies, diabodies or any other portion(s) of the antibody that retain the capability of the antibody to specifically bind to the antigen.

An “antibody” (e.g. a monoclonal antibody) or an “antigen-binding portion” may have been derivatized or be linked to a different molecule. For example, molecules that may be linked to the antibody are other proteins (e.g. other antibodies), a molecular label (e.g. a fluorescent, luminescent, colored or radioactive molecule), a pharmaceutical and/or a toxic agent. The antibody or antigen-binding portion may be linked directly (e.g. in form of a fusion between two proteins), or via a linker molecule (e.g. any suitable type of chemical linker known in the art).

As used herein, the terms “binding” or “bind” refer to specific binding to the antigen of interest (e.g. human GDF-15). Preferably, the Kd value is less than 100 nM, more preferably less than 50 nM, still more preferably less than 10 nM, still more preferably less than 5 nM and most preferably less than 2 nM.

The term “epitope” as used herein refers to a small portion of an antigen that forms the binding site for an antibody.

In the context of the present invention, for the purposes of characterizing the binding properties of antibodies, binding or competitive binding of antibodies or their antigen-binding portions to the antigen of interest (e.g. human GDF-15) is preferably measured by using surface plasmon resonance measurements as a reference standard assay, as described below.

The terms “K_(D)” or “K_(D) value” relate to the equilibrium dissociation constant as known in the art. In the context of the present invention, these terms relate to the equilibrium dissociation constant of an antibody with respect to a particular antigen of interest (e.g. human GDF-15) The equilibrium dissociation constant is a measure of the propensity of a complex (e.g. an antigen-antibody complex) to reversibly dissociate into its components (e.g. the antigen and the antibody). For the antibodies according to the invention, K_(D) values (such as those for the antigen human GDF-15) are preferably determined by using surface plasmon resonance measurements as described below.

An “isolated antibody” as used herein is an antibody that has been identified and separated from the majority of components (by weight) of its source environment, e.g. from the components of a hybridoma cell culture or a different cell culture that was used for its production (e.g. producer cells such as CHO cells that recombinantly express the antibody). The separation is performed such that it sufficiently removes components that may otherwise interfere with the suitability of the antibody for the desired applications (e.g. with a therapeutic use of the anti-human GDF-15 antibody according to the invention). Methods for preparing isolated antibodies are known in the art and include Protein A chromatography, anion exchange chromatography, cation exchange chromatography, virus retentive filtration and ultrafiltration. Preferably, the isolated antibody preparation is at least 70% pure (w/w), more preferably at least 80% pure (w/w), still more preferably at least 90% pure (w/w), still more preferably at least 95% pure (w/w), and most preferably at least 99% pure (w/w), as measured by using the Lowry protein assay.

A “diabody” as used herein is a small bivalent antigen-binding antibody portion which comprises a heavy chain variable domain linked to a light chain variable domain on the same polypeptide chain linked by a peptide linker that is too short to allow pairing between the two domains on the same chain. This results in pairing with the complementary domains of another chain and in the assembly of a dimeric molecule with two antigen binding sites. Diabodies may be bivalent and monospecific (such as diabodies with two antigen binding sites for human GDF-15), or may be bivalent and bispecific (e.g. diabodies with two antigen binding sites, one being a binding site for human GDF-15, and the other one being a binding site for a different antigen). A detailed description of diabodies can be found in Holliger P et al. (“”Diabodies“: small bivalent and bispecific antibody fragments.” Proc Natl Acad Sci U S A. 1993 Jul. 15; 90(14):6444-8.).

A “single-domain antibody” (which is also referred to as “Nanobody™”) as used herein is an antibody fragment consisting of a single monomeric variable antibody domain. Structures of and methods for producing single-domain antibodies are known from the art, e.g. from Holt L J et al. (“Domain antibodies: proteins for therapy.” Trends Biotechnol. 2003 November; 21(11):484-90.), Saerens D et al. (“Single-domain antibodies as building blocks for novel therapeutics.” Curr Opin Pharmacol. 2008 October; 8(5):600-8. Epub 2008 Aug. 22.), and Arbabi Ghahroudi M et al. (“Selection and identification of single domain antibody fragments from camel heavy-chain antibodies.” FEBS Lett. 1997 Sep. 15; 414(3):521-6.).

The terms “significant”, “significantly”, etc. as used herein refer to a statistically significant difference between values as assessed by appropriate methods known in the art, and as assessed by the methods referred to herein.

In accordance with the present invention, each occurrence of the term “comprising” may optionally be substituted with the term “consisting of”.

The terms “cancer” and “cancer cell” is used herein in accordance with their common meaning in the art (see for instance Weinberg R. et al.: The Biology of Cancer. Garland Science: New York 2006. 850p., which is incorporated herein by reference in its entirety).

The cancers, for which a prediction of a clinical outcome according to the present invention is provided, are solid cancers. A “solid cancer” is a cancer which forms one or more solid tumors. Such solid cancers forming solid tumors are generally known in the art. The term “solid cancer” encompasses both a primary tumor formed by the cancer and possible secondary tumors, which are also known as metastases. Preferred solid cancers are selected from the group consisting of melanoma, colorectal cancer, prostate cancer, head and neck cancer, urothelial cancer, stomach cancer, pancreatic cancer, liver cancer, testis cancer, ovarian cancer, endometrial cancer, cervical cancer, brain cancer, breast cancer, gastric cancer, renal cell carcinoma, Ewing's sarcoma, non-small cell lung cancer and small cell lung cancer, preferably selected from the group consisting of melanoma, colorectal cancer, prostate cancer, head and neck cancer, urothelial cancer, stomach cancer, pancreatic cancer, liver cancer, testis cancer, ovarian cancer, endometrial cancer and cervical cancer, more preferably selected from the group consisting of melanoma, colorectal cancer, prostate cancer, head and neck cancer, urothelial cancer and stomach cancer, and most preferably selected from the group consisting of melanoma, colorectal cancer and prostate cancer.

As referred to herein, the term “brain cancer” refers to all brain cancers known in the art. It includes but is not limited to glioma (WHO grade Ito IV), astrocytoma, meningioma and medulloblastoma.

As referred to herein, the term “head and neck cancer” refers to all head and neck cancers known in the art. It includes but is not limited to oesophagus carcinoma, oral squamous cell carcinoma and hypopharyngeal cancer. A particularly preferred head and neck cancer according to the invention is oral squamous cell carcinoma.

As used herein, terms such as “treatment of cancer” or “treating cancer” refer to a therapeutic treatment.

As referred to herein, a treatment of cancer can be a first-line therapy, a second-line therapy or a third-line therapy or a therapy that is beyond third-line therapy. The meaning of these terms is known in the art and in accordance with the terminology that is commonly used by the US National Cancer Institute.

A treatment of cancer does not exclude that additional or secondary therapeutic benefits also occur in patients. For example, an additional or secondary benefit may be an influence on cancer-induced weight loss. However it is understood that a “treatment of cancer” as referred to herein is for treating the cancer itself, and that any secondary or additional effects only reflect optional, additional advantages of the treatment of cancer.

As treatment of cancer as referred to in accordance with the invention is preferably a cancer immunotherapy. The term “cancer immunotherapy” is known in the art and generally relates to a treatment of cancer in which the immune system of the patient is used to treat the cancer. Cancer cells harbor genomic mutations which give rise to cancer cell antigens that are specific to the cancer cells and different from the antigens of non-cancerous cells. Thus, a cancer immunotherapy referred to in accordance with the invention is preferably a cancer immunotherapy wherein such cancer cell antigens are recognized by the immune system, and wherein cancer cells expressing these antigens are killed by the immune system. A cancer immunotherapy can be assessed by immunomonitoring methods known in the art, e.g. by measuring intracellular IFN-γ expression (e.g. in CD8⁺ T-cells and/or NK cells) in blood samples, measuring CD107a cell surface expression (e.g. on CD8⁺ T-cells and/or NK cells) in blood samples, measuring intracellular TNF-α expression (e.g. on leukocytes) in blood samples, intracellular Interleukin-2 expression (e.g. in CD8⁺ T-cells and/or in CD4⁺ T-cells) in blood samples, CD154 cell surface expression (e.g. in CD8⁺ T-cells and/or in CD4⁺ T-cells) in blood samples, tetramer or dextramer staining for tumor antigen-specific T cells in blood samples, CTL activity against autologous tumor cells or presence of T cells against neoantigens derived from tumor-specific mutations. Preferred methods to assess cancer immunotherapy are the methods according to Gouttefangeas C et al.: “Flow Cytometry in Cancer Immunotherapy: Applications, Quality Assurance and Future.” (2015) In: Cancer Immunology: Translational Medicine from Bench to Bedside (N. Rezaei editor). Springer. Chapter 25: pages 471-486; and the methods according to Van der Burg S H, et al.: “Immunoguiding, the final frontier in the immunotherapy of cancer.” (2014) In Cancer Immunotherapy meets oncology (C M Britten, S Kreiter, M. Diken & H G Rammensee eds). Springer International Publishing Switzerland p37-51 ISBN: 978-3-319-05103-1.

As used herein, a “cancer immunotherapy” optionally encompasses a treatment where in addition to the immune system which is used to treat the cancer, additional mechanisms of cancer treatment are used. One example of a cancer immunotherapy where additional mechanisms of cancer treatment can be used is a combination therapy with known chemotherapeutic agent(s). Such combination therapy with known chemotherapeutic agent(s) may, for instance, not only include the treatment of cancer in which the immune system is used to treat the cancer but also include a treatment of cancer in which the cancer cells are killed by said chemotherapeutic agent(s) directly.

As referred to herein, an “immune checkpoint blocker treatment” is a treatment with one or more immune checkpoint blockers as indicated below.

The methods for predicting according to the invention are preferably carried out prior to the start of the immune checkpoint blocker treatment.

Alternatively, the methods for predicting according to the invention can also be carried out at a point in time where the immune checkpoint blocker treatment has already been started.

Thus, it is to be understood that the methods for predicting according to the invention may be used for patients who are subject to an immune checkpoint blocker treatment of the solid cancer or a different treatment of the solid cancer (e.g. a treatment with other agents which are pharmaceutically active against cancer). It is, however, also understood that neither immune checkpoint blocker treatment steps nor any other treatment steps form part of the methods for predicting according to the present invention.

As referred to herein, an agent pharmaceutically active against cancer may, for instance, be a known anticancer agent and/or an immune-stimulatory molecule. Known anticancer agents include but are not limited to alkylating agents such as cisplatin, carboplatin, oxaliplatin, mechlorethamine, cyclophosphamide, chlorambucil, and ifosfamide; anti-metabolites such as azathioprine and mercaptopurine; alkaloids such as vinca alkaloids (e.g. vincristine, vinblastine, vinorelbine, and vindesine), taxanes (e.g. paclitaxel, docetaxel) etoposide and teniposide; topoisomerase inhibitors such as camptothecins (e.g. irinotecan and topotecan); cytotoxic antibiotics such as actinomycin, anthracyclines, doxorubicin, daunorubicin, valrubicin, idarubicin, epirubicin, bleomycin, plicamycin and mitomycin; and radioisotopes.

Blood Samples

As referred to herein, the term “blood sample” includes, without limitation, whole blood, serum and plasma samples. It also includes other sample types such as blood fractions other than serum and plasma. Such samples and fractions are known in the art.

Blood samples which are used for the methods according to the invention can be any types of blood samples which contain hGDF-15. Suitable types of blood samples containing hGDF-15 are known in the art and include serum and plasma samples. Alternatively, further types of blood samples which contain hGDF-15 can also be readily identified by the skilled person, e.g. by measuring whether hGDF-15 is contained in these samples, and which levels of hGDF-15 are contained in these samples, by using the methods disclosed herein.

If the human blood sample obtained from the human patient is a sample from a patient who has received said immune checkpoint blocker, the blood sample obtained from the human patient may contain the immune checkpoint blocker and/or biological metabolites thereof.

Immune checkpoint blockers are known in the art, and their presence can be determined by the skilled person. Examples of metabolites of drugs such as metabolites of therapeutic antibodies are also known and can be detected, e.g. by using appropriate secondary antibodies.

Clinical Outcomes

According to the invention, levels of hGDF-15 in human blood samples can be used to predict the probability of a positive clinical outcome of a treatment with an immune checkpoint blocker in a human cancer patient.

As referred to herein, a “positive clinical outcome” can be any therapeutic indicator for a therapeutic treatment benefit. Such indicators are well-known in the art.

Thus, according to the invention, in a preferred embodiment, a positive clinical outcome can be a treatment response of the human cancer patient to the immune checkpoint blocker treatment.

In one preferred aspect of this embodiment, the presence or absence of a treatment response is assessed by an assessment of whether the treatment inhibits cancer growth in the treated patient or patients. Preferably, the inhibition is statistically significant as assessed by appropriate statistical tests which are known in the art. Inhibition of cancer growth may be assessed by comparing cancer growth in a group of patients treated in accordance with the present invention to a control group of untreated patients, or by comparing a group of patients that receive a standard cancer treatment of the art plus a treatment according to the invention with a control group of patients that only receive a standard cancer treatment of the art. Such studies for assessing the inhibition of cancer growth are designed in accordance with accepted standards for clinical studies, e.g. double-blinded, randomized studies with sufficient statistical power.

The term “cancer growth” as used herein relates to any measureable growth of the cancer. For cancers forming solid tumors, “cancer growth” relates to a measurable increase in tumor volume over time. If the cancer has formed only a single tumor, “cancer growth” relates only to the increase in volume of the single tumor. If the cancer has formed multiple tumors such as metastases, “cancer growth” relates to the increase in volume of all measurable tumors. For solid tumors, the tumor volume can be measured by any method known in the art, including magnetic resonance imaging and computed tomography (CT scan).

In a very preferred aspect of this embodiment, an assessment of a response to the treatment is made based on a classification of responders and non-responders by using the response evaluation criteria in solid tumours, version 1.1 (RECIST v1.1) (Eisenhauer et al.: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). In: Eur. J. Cancer. 45, No. 2, January 2009, pp. 228-47). Likewise, these criteria are also very preferred for the prediction methods according to the invention.

Appropriate time periods for an assessment of a response are known in the art and will be chosen by the skilled person with due regard to known factors such as the particular solid cancer and the severity of said cancer, and the respective stage of the cancer disease. For example, a treatment response may be assessed at a time point of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 18 and/or 24 months after start of the treatment with the immune checkpoint blocker. Preferably, a treatment response is assessed after 12 weeks or after 4 months or after 6 months after start of the treatment with the immune checkpoint blocker. Conversely, a prediction of a probability of a treatment response according to the invention may be provided for one or more of the above time points.

In another preferred embodiment, a positive clinical outcome can be survival of the human cancer patient following the immune checkpoint blocker treatment. Survival of patient groups can be analysed by methods known in the art, e.g. by Kaplan-Meier curves.

Appropriate time periods for the assessment of survival are known in the art and will be chosen by the skilled person with due regard to known factors such as the particular solid cancer and the severity of said cancer, and the respective stage of the cancer disease. For example, survival, preferably short-term survival, may, for instance, be assessed at a time point of 1 month, 6 weeks, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months and/or 18 months after start of the treatment with the immune checkpoint blocker. Short-term survival is preferably assessed after 6 weeks, 2 months, 3 months, 4 months, 5 months, or 6 months after start of the treatment with the immune checkpoint blocker. Alternatively, survival, preferably long-term survival, may, for instance, be assessed at a time point of 24 months, 30 months, 36 months, 42 months, 48 months, 54 months, 60 months, 6 years, 7 years, 8 years, 9 years and/or 10 years after start of the treatment with the immune checkpoint blocker. In melanoma, long-term survival is preferably assessed at a time point of 36 months after start of the treatment with the immune checkpoint blocker. Conversely, a prediction of a probability of survival according to the invention may be provided for one or more of these time points.

According to the invention, in a further embodiment, a “positive clinical outcome” can be the absence of disease progression of the human cancer patient following the immune checkpoint blocker treatment. Indicators for the presence and absence of disease progression are known in the art and will be chosen by the skilled person with due regard to the respective solid cancer and the respective stage of the cancer disease.

Predicting the Probability of a Positive Clinical Outcome According to the Invention

For predicting the probability of a positive clinical outcome according to the invention based on hGDF-15 levels, the methods for predicting, which are defined above, are preferably used.

In order to practice the methods of the invention, statistical methods known in the art can be employed.

In these methods, patient data from one or more clinical studies, which have treated patients suffering from solid cancers with immune checkpoint blockers, can be used as a basis to generate statistical models. These models can then be used to determine appropriate hGDF-15 threshold levels for predicting the probability of a positive clinical outcome.

For instance, survival can be analyzed by Cox proportional hazard survival models, e.g. by fitting the model with the hGDF-15 level (ng/ml) as continuous predictor.

In a preferred embodiment, the probability of survival is predicted using a Hazard ratio (HR) with overall survival (time to death) as outcome variable and GDF-15 as continuous predictor, wherein it is predicted that per 1 ng/ml increase in GDF-15 serum levels, the risk to die increases by a factor of 1.27 (95% confidence interval 1.10-1.47, P=0.00109).

In an alternative embodiment, the probability of survival is predicted using a grouping variable based on GDF-15 as categorical predictor, e.g. with groups <1.8 ng/ml, 1.8{4.2 ng/ml, >4.2 ng/ml).

Preferred statistical methods, which can be used according to the invention to generate statistical models of patient data from clinical studies, are disclosed in Example 1. Considering the highly significant effects, less accurate statistical models are also suitable. It is understood that the statistical methods disclosed in Example 1 are not limited to the particular features of Example 1 such as the type of cancer (melanoma), the type of immune checkpoint blocker and the particular statistical values obtained in the Example. Rather, these methods disclosed in Example 1 can generally be used in connection with any embodiment of the present invention.

hGDF-15 Levels

According to the invention, there is an inverse relationship between hGDF-15 levels and the probability of a positive clinical outcome (e.g. probability of survival or probability of a treatment response) in human cancer patients treated with immune checkpoint blockers. Thus, according to the invention, a decreased level of hGDF-15 in said human blood sample indicates an increased probability of a positive clinical outcome (e.g. probability of survival or probability of a treatment response) in said human cancer patients.

Thus, as used herein, terms such as “a decreased level of hGDF-15 in said human blood sample indicates an increased probability” mean that the level of hGDF-15 in said human blood sample and the probability of a positive clinical outcome (e.g. probability of survival or probability of a treatment response) in human cancer patients treated with immune checkpoint blockers follow an inverse relationship. Thus, the higher the level of hGDF-15 in said human blood sample is, the lower is the probability of a positive clinical outcome (e.g. probability of survival or probability of a treatment response).

For instance, in connection with the methods for predicting according to the invention defined herein, hGDF-15 threshold levels can be used.

According to the invention, the inverse relationship between hGDF-15 levels and a positive clinical outcome applies to any threshold value, and hence the invention is not limited to particular threshold values.

Preferable hGDF-15 threshold levels are hGDF-15 serum levels as defined above in the preferred embodiments.

Alternatively, hGDF-15 threshold levels according to the present invention can be obtained, and/or further adjusted, by using the above-mentioned statistical methods for predicting the probability of a positive clinical outcome.

An hGDF-15 threshold level may be a single hGDF-15 threshold level. The invention also encompasses the use of more than one hGDF-15 threshold level, e.g. 2, 3, 4, 5, 6, 7, 8, 9, 10 or more hGDF-15 threshold levels. The invention also encompasses the use of a continuum of hGDF-15 threshold levels. A non-limiting example of such a continuum of hGDF-15 threshold levels is given in FIG. 3.

For each single hGDF-15 threshold level of the one or more hGDF-15 threshold levels, a corresponding probability of a positive clinical outcome (e.g. probability of survival or probability of a treatment response) can be predicted. If the level of hGDF-15 in the blood sample is decreased compared to such as a hGDF-15 threshold level according to the invention, this indicates that the probability of a positive clinical outcome (e.g. the probability of survival or the probability of a treatment response) is increased, i.e. increased compared to the probability of a positive clinical outcome which can be predicted for said hGDF-15 threshold level. In a non-limiting Example, FIG. 3 of Example 1 illustrates a continuum of hGDF-15 threshold levels and a curve of corresponding predicted probabilities for a treatment response.

In a further non-limiting Example, the probability of a treatment response can be predicted based on an odds ratio such as the odds ratio shown in Table 2. Thus, the probability of a treatment response can be predicted using an odds ratio of 0.389 for serum levels of hGDF-15 in ng/ml as a continuous predictor with a confidence interval of from 0.159 to 0.698.

hGDF-15 levels in blood samples can be measured by any methods known in the art. For instance, a preferred method of measuring hGDF-15 levels in blood samples including serum levels is a measurement of hGDF-15 levels by Enzyme-Linked Immunosorbent Assay (ELISA) using antibodies to GDF-15 or by Western Blotting using antibodies to GDF-15 (e.g. Western Blotting from concentrated serum). Such ELISA methods are exemplified in Example 1, but can also include bead-based methods like the Luminex technology and others. Alternatively, hGDF-15 levels in blood samples including serum levels may be determined by known electrochemiluminesence immunoassays using antibodies to GDF-15. For instance, the Roche Elecsys® technology can be used for such electrochemiluminesence immunoassays. Other possible methods include antibody-based detection from bodily fluids after separation of proteins in an electrical field.

The median hGDF-15 serum level of healthy human control individuals is <0.8 ng/ml. The expected range is between 0.2 ng/ml and 1.2 ng/ml in healthy human controls (Reference: Tanno T et al.: “Growth differentiation factor 15 in erythroid health and disease.” Curr Opin Hematol. 2010 May; 17(3): 184-190.).

According to the invention, a preferred hGDF-15 threshold level is a hGDF-15 serum level selected from the range of between 1.2 ng/ml and 8.0 ng/ml, or a hGDF-15 level selected from the range of between 1.5 ng/ml and 7.0 ng/ml, or a hGDF-15 level selected from the range of between 2.0 ng/ml and 6.0 ng/ml, or a hGDF-15 level selected from the range of between 2.5 ng/ml and 5.0 ng/ml, or a hGDF-15 level selected from the range of between 3.0 ng/ml and 4.0 ng/ml.

In a preferred embodiment, the cancer of the patient is melanoma. In a preferred aspect of this embodiment, the hGDF-15 threshold level is a hGDF-15 level selected from the range of between 3.0 ng/ml and 4.0 ng/ml, preferably a hGDF-15 level selected from the range of between 3.2 ng/ml and 3.7 ng/ml, and most preferably a hGDF-15 level of 3.4 ng/ml.

It is understood that for these hGDF-15 serum levels, and based on the disclosure of the invention provided herein, corresponding hGDF-15 levels in other blood samples can be routinely obtained by the skilled person (e.g. by comparing the relative level of hGDF-15 in serum with the respective level in other blood samples). Thus, the present invention also encompasses preferred hGDF-15 levels in plasma, whole blood and other blood samples, which correspond to each of the preferred hGDF-15 serum levels and ranges indicated above.

Antibodies Capable of Binding to hGDF-15 Which Can be Used in Accordance With the Invention

The methods, apparatuses and kits of the invention may use one or more antibodies capable of binding to hGDF-15 or an antigen-binding portion thereof, as defined above.

It was previously shown that human GDF-15 protein can be advantageously targeted by a monoclonal antibody (WO2014/049087, which is incorporated herein by reference in its entirety), and that such antibody has advantageous properties including a high binding affinity to human GDF-15, as demonstrated by an equilibrium dissociation constant of about 790 pM for recombinant human GDF-15 (see Reference Example 1). Thus, in a preferred embodiment, the invention uses an antibody capable of binding to hGDF-15, or an antigen-binding portion thereof. Preferably, the antibody is a monoclonal antibody capable of binding to hGDF-15, or an antigen-binding portion thereof.

Thus, in a more preferred embodiment, the antibody capable of binding to hGDF-15 or antigen-binding portion thereof in accordance with the invention is a monoclonal antibody capable of binding to human GDF-15, or an antigen-binding portion thereof, wherein the heavy chain variable domain comprises a CDR3 region comprising the amino acid sequence of SEQ ID NO: 5 or an amino acid sequence at least 90% identical thereto, and wherein the light chain variable domain comprises a CDR3 region comprising the amino acid sequence of SEQ ID NO: 7 or an amino acid sequence at least 85% identical thereto. In this embodiment, preferably, the antibody or antigen-binding portion thereof comprises a heavy chain variable domain which comprises a CDR1 region comprising the amino acid sequence of SEQ ID NO: 3 and a CDR2 region comprising the amino acid sequence of SEQ ID NO: 4, and the antibody or antigen-binding portion thereof comprises a light chain variable domain which comprises a CDR1 region comprising the amino acid sequence of SEQ ID NO: 6, and a CDR2 region comprising the amino acid sequence ser-ala-ser.

Thus, in a still more preferred embodiment, the antibody capable of binding to hGDF-15 or antigen-binding portion thereof in accordance with the invention is a monoclonal antibody capable of binding to human GDF-15, or an antigen-binding portion thereof, wherein the antibody or antigen-binding portion thereof comprises a heavy chain variable domain which comprises a CDR1 region comprising the amino acid sequence of SEQ ID NO: 3, a CDR2 region comprising the amino acid sequence of SEQ ID NO: 4 and a CDR3 region comprising the amino acid sequence of SEQ ID NO: 5, and wherein the antibody or antigen-binding portion thereof comprises a light chain variable domain which comprises a CDR1 region comprising the amino acid sequence of SEQ ID NO: 6, a CDR2 region comprising the amino acid sequence ser-ala-ser and a CDR3 region comprising the amino acid sequence of SEQ ID NO: 7.

In another embodiment in accordance with the above embodiments of the monoclonal antibody capable of binding to human GDF-15, or an antigen-binding portion thereof, the heavy chain variable domain comprises a region comprising an FR1, a CDR1, an FR2, a CDR2 and an FR3 region and comprising the amino acid sequence of SEQ ID NO: 1 or a sequence 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, ₉₈% _(or 99)% identical thereto, and the light chain variable domain comprises a region comprising an FR1, a CDR1, an FR2, a CDR2 and an FR3 region and comprising the amino acid sequence of SEQ ID NO: 2 or a sequence 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical thereto.

In another embodiment in accordance with the above embodiments of the monoclonal antibody capable of binding to human GDF-15, or an antigen-binding portion thereof, the heavy chain variable domain comprises a CDR1 region comprising the amino acid sequence of SEQ ID NO: 3 and a CDR2 region comprising the amino acid sequence of SEQ ID NO: 4, and the light chain variable domain comprises a CDR1 region comprising the amino acid sequence of SEQ ID NO: 6 and a CDR2 region comprising the amino acid sequence of SEQ ID NO: 7. In a preferred aspect of this embodiment, the antibody may have CDR3 sequences as defined in any of the embodiments of the invention described above.

In another embodiment in accordance with the monoclonal antibody capable of binding to human GDF-15, or an antigen-binding portion thereof, the antigen-binding portion is a single-domain antibody (also referred to as “Nanobody™”). In one aspect of this embodiment, the single-domain antibody comprises the CDR1, CDR2, and CDR3 amino acid sequences of SEQ ID NO: 3, SEQ ID NO: 4, and SEQ ID NO: 5, respectively. In another aspect of this embodiment, the single-domain antibody comprises the CDR1, CDR2, and CDR3 amino acid sequences of SEQ ID NO: 6, ser-ala-ser, and SEQ ID NO: 7, respectively. In a preferred aspect of this embodiment, the single-domain antibody is a humanized antibody.

Preferably, the antibodies capable of binding to human GDF-15 or the antigen-binding portions thereof have an equilibrium dissociation constant for human GDF-15 that is equal to or less than 100 nM, less than 20 nM, preferably less than 10 nM, more preferably less than 5 nM and most preferably between 0.1 nM and 2 nM.

In another embodiment in accordance with the above embodiments of the monoclonal antibody capable of binding to human GDF-15, or an antigen-binding portion thereof, the antibody capable of binding to human GDF-15 or the antigen-binding portion thereof binds to the same human GDF-15 epitope as the antibody to human GDF-15 obtainable from the cell line B1-23 deposited with the Deutsche Sammlung für Mikroorganismen and Zellkulturen GmbH (DMSZ) under the accession No. DSM ACC3142. As described herein, antibody binding to human GDF-15 in accordance with the present invention is preferably assessed by surface plasmon resonance measurements as a reference standard method, in accordance with the procedures described in Reference Example 1. Binding to the same epitope on human GDF-15 can be assessed similarly by surface plasmon resonance competitive binding experiments of the antibody to human GDF-15 obtainable from the cell line B1-23 and the antibody that is expected to bind to the same human GDF-15 epitope as the antibody to human GDF-15 obtainable from the cell line B1-23.

In a very preferred embodiment, the antibody capable of binding to human GDF-15 or the antigen-binding portion thereof is a monoclonal antibody capable of binding to human GDF-15, or an antigen-binding portion thereof, wherein the binding is binding to a conformational or discontinuous epitope on human GDF-15 comprised by the amino acid sequences of SEQ ID No: 25 and SEQ ID No: 26. In a preferred aspect of this embodiment, the antibody or antigen-binding portion thereof is an antibody or antigen-binding portion thereof as defined by the sequences of any one of the above embodiments.

In a further embodiment in accordance with the above embodiments, antibodies including the antibody capable of binding to human GDF-15 or the antigen-binding portion thereof can be modified, e.g. by a tag or a label.

A tag can, for instance, be a biotin tag or an amino acid tag. Non-limiting examples of such acid tag tags include Polyhistidin (His-) tags, FLAG-tag, Hemagglutinin (HA) tag, glycoprotein D (gD) tag, and c-myc tag. Tags may be used for various purposes. For instance, tags may be used to assist purification of the antibody capable of binding to human GDF-15 or the antigen-binding portion thereof. Preferably, such tags are present at the C-terminus or N-terminus of the antibody capable of binding to human GDF-15 or the antigen-binding portion thereof.

As used herein, the term “label” relates to any molecule or group of molecules which can facilitate detection of the antibody. For instance, labels may be enzymatic such as horseradish peroxidase (HRP), alkaline phosphatase (AP) or glucose oxidase. Enzymatically labelled antibodies may, for instance, be employed in enzyme-linked immunosorbent assays. Labels may also be radioactive isotopes, DNA sequences (which may, for instance, be used to detect the antibodies by polymerase chain reaction (PCR)), fluorogenic reporters and electrochemiluminescent groups (e.g. ruthenium complexes). As an alternative to labelling, antibodies used according to the invention, in particular an antibody capable of binding to human GDF-15 or the antigen-binding portion thereof, can be detected directly, e.g. by surface plasmon resonance measurements.

Immune Checkpoint Blockers

The present invention relates to the prediction of the probability of a positive clinical outcome of a treatment with an immune checkpoint blocker in human cancer patient, in particular to the prediction of the probability of a treatment response of a human cancer patient to an immune checkpoint blocker treatment and to the prediction of the probability of survival of a human cancer patient following an immune checkpoint blocker treatment.

Cancer cells harbor genomic mutations which give rise to cancer cell antigens that are specific to the cancer cells and different from the antigens of non-cancerous cells. Therefore, an intact immune system which is not inhibited should recognize these cancer cell antigens, such that an immune response against these antigens is elicited. However, most cancers have developed immune tolerance mechanisms against these antigens. One class of mechanisms by which cancer cells achieve such immune tolerance is the utilization of immune checkpoints.

An “immune checkpoint” as used herein generally means an immunological mechanism by which an immune response can be inhibited. More particularly, an immune checkpoint is a mechanism which is characterized in that a molecule of the immune system (or a group of molecules of the immune system) inhibits the immune response by inhibiting the activation of cells of the immune system. Such molecule (or group of molecules) of the immune system which inhibits (inhibit) the immune response by inhibiting the activation of cells of the immune system is (are) also known as checkpoint molecule(s).

As used herein, an “immune checkpoint blocker” is a molecule which is capable of blocking an immune checkpoint. The term “immune checkpoint blocker” as used herein does not refer to an hGDF-15 inhibitor such as an antibody capable of binding to hGDF-15 but means a molecule which is different from an hGDF-15 inhibitor.

The most common immune checkpoint blockers which are known to date are inhibitors of immune checkpoint molecules such as inhibitors of human PD-1 and inhibitors of human PD-L1. Further immune checkpoint blockers are anti-LAG-3, anti-B7H3, anti-TIM3, anti-VISTA, anti-TIGIT, anti-KIR, anti-CD27, anti-CD137 as well as inhibitors of IDO. Therefore, as referred to by the present invention, a preferred form of an immune checkpoint blocker is an inhibitor of an immune checkpoint molecule. Alternatively, an immune checkpoint blocker can be an activator of a co-stimulating signal which overrides the immune checkpoint.

Preferred immune checkpoint blockers are inhibitors of human PD-1 and inhibitors of human PD-L1. In one preferred embodiment in accordance with all of the embodiments of the invention, the immune checkpoint blocker is not an inhibitor of human CTLA4.

As used herein, an “inhibitor of human PD-1” can be any molecule which is capable of specifically inhibiting the function of human PD-1. Non-limiting examples of such molecules are antibodies capable of binding to human PD-1 and DARPins (Designed Ankyrin Repeat Proteins) capable of binding to human PD-1. Preferably, the inhibitor of PD-1 referred to by the invention is an antibody capable of binding to human PD-1, more preferably a monoclonal antibody capable of binding to human PD-1. Most preferably, the monoclonal antibody capable of binding to human PD-1 is selected from the group consisting of nivolumab, pembrolizumab, pidilizumab and AMP-224.

As used herein, an “inhibitor of human PD-L1” can be any molecule which is capable of specifically inhibiting the function of human PD-L1. Non-limiting examples of such molecules are antibodies capable of binding to human PD-L1 and DARPins (Designed Ankyrin Repeat Proteins) capable of binding to human PD-L1. Preferably, the inhibitor of human PD-L1 referred to by the invention is an antibody capable of binding to human PD-L1, more preferably a monoclonal antibody capable of binding to human PD-L1. Most preferably, the monoclonal antibody capable of binding to human PD-L1 is selected from the group consisting of BMS-936559, MPDL3280A, MED14736, and MSB0010718C.

Methods and Techniques

Generally, unless otherwise defined herein, the methods used in the present invention (e.g. cloning methods or methods relating to antibodies) are performed in accordance with procedures known in the art, e.g. the procedures described in Sambrook et al. (“Molecular Cloning: A Laboratory Manual.”, 2^(nd) Ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York 1989), Ausubel et al. (“Current Protocols in Molecular Biology.” Greene Publishing Associates and Wiley Interscience; New York 1992), and Harlow and Lane (“Antibodies: A Laboratory Manual” Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York 1988), all of which are incorporated herein by reference.

Binding of antibodies to their respective target proteins can be assessed by methods known in the art. The binding of monoclonal antibodies to their respective targets is preferably assessed by surface plasmon resonance measurements. These measurements are preferably carried out by using a Biorad ProteOn XPR36 system and Biorad GLC sensor chips, as exemplified for anti-human GDF-15 mAb-B1-23 in Reference Example 1.

Sequence Alignments of sequences according to the invention are performed by using the BLAST algorithm (see Altschul et al.(1990) “Basic local alignment search tool.” Journal of Molecular Biology 215. p. 403-410.; Altschul et al.: (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25:3389-3402, all of which are incorporated herein by reference). Preferably, the following parameters are used: Max target sequences 10; Word size 3; BLOSUM 62 matrix; gap costs: existence 11, extension 1; conditional compositional score matrix adjustment. Thus, when used in connection with sequences, terms such as “identity” or “identical” refer to the identity value obtained by using the BLAST algorithm.

Monoclonal antibodies according to the invention can be produced by any method known in the art, including but not limited to the methods referred to in Siegel DL (“Recombinant monoclonal antibody technology.” Transfus Clin Biol. 2002 January; 9(1):15-22, which is incorporated herein by reference). In one embodiment, an antibody according to the invention is produced by the hybridoma cell line B1-23 deposited with the Deutsche Sammlung für Mikroorganismen and Zellkulturen GmbH (DSMZ) under the accession No. DSM ACC3142 under the Budapest treaty. The deposit was filed on Sep. 29, 2011.

Levels of human GDF-15 (hGDF-15) can be measured by any method known in the art, including measurements of hGDF-15 protein levels by methods including (but not limited to) mass spectrometry for proteins or peptides derived from human GDF-15, Western Blotting using antibodies specific to human GDF-15, strip tests using antibodies specific to human GDF-15, or immunocytochemistry using antibodies specific to human GDF-15. A preferred method of measuring hGDF-15 serum levels is a measurement of hGDF-15 serum levels by Enzyme-Linked Immunosorbent Assay (ELISA) by using antibodies to GDF-15. Such ELISA methods are exemplified in Example 1. Alternatively, hGDF-15 serum levels may be determined by known electrochemiluminesence immunoassays using antibodies to GDF-15. For instance, the Roche Elecsys® technology can be used for such electrochemiluminesence immunoassays.

Apparatuses of the Invention The invention also relates to the apparatuses defined above.

An apparatus of the invention can be any apparatus which is configured to perform the methods of the invention.

As used herein, the term “configured to perform” means that the apparatus us specifically configured for the recited method steps. For instance, an apparatus configured to perform a method which uses a particular threshold level will be specifically configured to use that particular threshold. For ELISA measurement, any reader able to measure absorption would be suitable. For bead-based assays, a Luminex analyser or a flow cytometer could be used.

In a preferred embodiment, the apparatus is an electrochemiluminescence (Elecsys®) analyzer such as a Cobas® analyzer, including but not limited to analyzers of the Cobas® 4000, Cobas® 6000, Cobas® 8000, Cobas c 111, and the Cobas INTEGRA® 400 plus series.

Kits of the Invention

The invention also relates to the kits defined above.

The recombinant hGDF-15 contained in the kits may be present in a form which can conveniently be used for calibration purposes. For instance, it may be present in the form of stock solutions which cover several concentrations in the range of 0 to 15 ng/ml, e.g. at least one concentration in the range of 0-1 ng/ml, at least one concentration in the range of 1-3 ng/ml, at least one concentration in the range of 3-6 ng/ml, and preferably at least one further concentration in the range of 6-10 ng/ml, and more preferably further comprising at least one further concentration in the range of 10-15 ng/ml.

Calibration with multiple hGDF-15 solutions at these concentrations will be particularly advantageous for an accurate measurement due to the high concentrations of hGDF-15 observed in sera of patients with poor predicted clinical outcome.

Sequences The amino acid sequences referred to in the present application are as follows (in an N-terminal to C-terminal order; represented in the one-letter amino acid code):

SEQ ID No: 1 (Region of the Heavy Chain Variable Domain comprising an FR1, a CDR1, an FR2, a CDR2 and an FR3 region from the Polypeptide Sequence of monoclonal anti-human GDF-15 mAb-B1-23): QVKLQQSGPGILQSSQTLSLTCSFSGFSLSTSGMGVSWIRQPSGKGLEWLAHIYWDDDKRYNPTLKSRLTISK DPSRNQVFLKITSVDTADTATYYC SEQ ID No: 2 (Region of the Light Chain Variable Domain comprising an FR1, a CDR1, an FR2, a CDR2 and an FR3 region from the Polypeptide Sequence of monoclonal anti-human GDF-15 mAb-B1-23): DIVLTQSPKFMSTSVGDRVSVTCKASQNVGTNVAWFLQKPGQSPKALIYSASYRYSGVPDRFTGSGSGTDFT LTISNVQSEDLAEYFC SEQ ID No: 3 (Heavy Chain CDR1 Region Peptide Sequence of monoclonal anti-human GDF-15 mAb-B1-23): GFSLSTSGMG SEQ ID No: 4 (Heavy Chain CDR2 Region Peptide Sequence of monoclonal anti-human GDF-15 mAb-B1-23): IYWDDDK SEQ ID No: 5 (Heavy Chain CDR3 Region Peptide Sequence of monoclonal anti-human GDF-15 mAb-B1-23): ARSSYGAMDY SEQ ID No: 6 (Light Chain CDR1 Region Peptide Sequence of monoclonal anti-human GDF-15 mAb-B1-23): QNVGTN Light Chain CDR2 Region Peptide Sequence of monoclonal anti-human GDF-15 mAb-B1-23: SAS SEQ ID No: 7 (Light Chain CDR3 Region Peptide Sequence of monoclonal anti-human GDF-15 mAb-B1-23): QQYNNFPYT SEQ ID No: 8 (recombinant mature human GDF-15 protein): GSARNGDHCPLGPGRCCRLHTVRASLEDLGWADWVLSPREVQVTMCIGACPSQFRAANMHAQIKTSLHRLK PDTVPAPCCVPASYNPMVLIQKTDTGVSLQTYDDLLAKDCHCI SEQ ID No: 9 (human GDF-15 precursor protein): MPGQELRTVNGSQMLLVLLVLSWLPHGGALSLAEASRASFPGPSELHSEDSRFRELRKRYEDLLTRLRANQS WEDSNTDLVPAPAVRILTPEVRLGSGGHLHLRISRAALPEGLPEASRLHRALFRLSPTASRSWDVTRPLRRQL SLARPQAPALHLRLSPPPSQSDQLLAESSSARPQLELHLRPQAARGRRRARARNGDHCPLGPGRCCRLHTV RASLEDLGWADWVLSPREVQVTMCIGACPSQFRAANMHAQIKTSLHRLKPDTVPAPCCVPASYNPMVLIQKT DTGVSLQTYDDLLAKDCHCI SEQ ID No: 10 (human GDF-15 precursor protein + N-terminal and C-terminal GSGS linker): GSGSGSGMPGQELRTVNGSQMLLVLLVLSWLPHGGALSLAEASRASFPGPSELHSEDSRFRELRKRYEDLL TRLRANQSWEDSNTDLVPAPAVRILTPEVRLGSGGHLHLRISRAALPEGLPEASRLHRALFRLSPTASRSWDV TRPLRRQLSLARPQAPALHLRLSPPPSQSDQLLAESSSARPQLELHLRPQAARGRRRARARNGDHCPLGPG RCCRLHTVRASLEDLGWADWVLSPREVQVTMCIGACPSQFRAANMHAQIKTSLHRLKPDTVPAPCCVPASY NPMVLIQKTDTGVSLQTYDDLLAKDCHCIGSGSGSG SEQ ID No: 11 (Flag peptide): DYKDDDDKGG SEQ ID No: 12 (HA peptide): YPYDVPDYAG SEQ ID No: 13 (peptide derived from human GDF-15): ELHLRPQAARGRR SEQ ID No: 14 (peptide derived from human GDF-15): LHLRPQAARGRRR SEQ ID No: 15 (peptide derived from human GDF-15): HLRPQAARGRRRA SEQ ID No: 16 (peptide derived from human GDF-15): LRPQAARGRRRAR SEQ ID No: 17 (peptide derived from human GDF-15): RPQAARGRRRARA SEQ ID No: 18 (peptide derived from human GDF-15): PQAARGRRRARAR SEQ ID No: 19 (peptide derived from human GDF-15): QAARGRRRARARN SEQ ID No: 20 (peptide derived from human GDF-15): MHAQIKTSLHRLK SEQ ID No: 25 (GDF-15 peptide comprising part of the GDF-15 Epitope that binds to B1-23): EVQVTMCIGACPSQFR SEQ ID No: 26 (GDF-15 peptide comprising part of the GDF-15 Epitope that binds to B1-23): TDTGVSLQTYDDLLAKDCHCI

The nucleic acid sequences referred to in the present application are as follows (in a 5′ to 3′ order; represented in accordance with the standard nucleic acid code):

SEQ ID No: 21 (DNA nucleotide sequence encoding the amino acid sequence defined in SEQ ID No: 1): CAAGTGAAGCTGCAGCAGTCAGGCCCTGGGATATTGCAGTCCTCCCAGACCCTCAGTCTGACTTGTTCT TTCTCTGGGTTTTCACTGAGTACTTCTGGTATGGGTGTGAGCTGGATTCGTCAGCCTTCAGGAAAGGGTC TGGAGTGGCTGGCACACATTTACTGGGATGATGACAAGCGCTATAACCCAACCCTGAAGAGCCGGCTCA CAATCTCCAAGGATCCCTCCAGAAACCAGGTATTCCTCAAGATCACCAGTGTGGACACTGCAGATACTGC CACATACTACTGT SEQ ID No: 22 (DNA nucleotide sequence encoding the amino acid sequence defined in SEQ ID No: 2): GACATTGTGCTCACCCAGTCTCCAAAATTCATGTCCACATCAGTAGGAGACAGGGTCAGCGTCACCTGCA AGGCCAGTCAGAATGTGGGTACTAATGTGGCCTGGTTTCTACAGAAACCAGGGCAATCTCCTAAAGCACT TATTTACTCGGCATCCTACCGGTACAGTGGAGTCCCTGATCGCTTCACAGGCAGTGGATCTGGGACAGA TTTCACTCTCACCATCAGCAACGTGCAGTCTGAAGACTTGGCAGAGTATTTCTGT SEQ ID No: 23 (DNA nucleotide sequence encoding the amino acid sequence defined in SEQ ID No: 5): GCTCGAAGTTCCTACGGGGCAATGGACTAC SEQ ID No: 24 (DNA nucleotide sequence encoding the amino acid sequence defined in SEQ ID No: 7): CAGCAATATAACAACTTTCCGTACACG

EXAMPLES

Reference Examples 1 to 3 exemplify an antibody to hGDF-15, which can be used in the methods, kits, and in the apparatuses according to the invention. This hGDF-15 antibody is a monoclonal antibody which is known from WO 2014/049087, which is incorporated herein by reference in its entirety.

Reference Example 1: Generation and Characterization of the GDF-15 Antibody B1-23

The antibody B1-23 was generated in a GDF-15 knock out mouse. Recombinant human GDF-15 (SEQ ID No: 8) was used as the immunogen.

The hybridoma cell line B1-23 producing mAb-B1-23 was deposited by the Julius-Maximilians-Universität Würzburg, Sanderring 2, 97070 Würzburg, Germany, with the Deutsche Sammlung für Mikroorganismen and Zellkulturen GmbH (DMSZ) under the accession No. DSM ACC3142, in accordance with the Budapest Treaty.

By means of a commercially available test strip system, B1-23 was identified as an IgG2a (kappa chain) isotype. Using surface plasmon resonance measurements, the dissociation constant (Kd) was determined as follows:

Binding of the monoclonal anti-human-GDF-15 antibody anti-human GDF-15 mAb-B1-23 according to the invention was measured by employing surface plasmon resonance measurements using a Biorad ProteOn XPR36 system and Biorad GLC sensor chips:

For preparing the biosensors recombinant mature human GDF-15 protein was immobilized on flow cells 1 and 2. On one flow cell recombinant GDF-15 derived from Baculvirus-transfected insect cells (HighFive insect cells) and on the other recombinant protein derived from expression in E. coli was used. The GLC sensor chip was activated using Sulfo-NHS (N-Hydroxysulfosuccinimide) and EDC (1-Ethyl-3-[3-dimethylaminopropyl]carbodiimide hydrochloride) (Biorad ProteOn Amine Coupling Kit) according to the manufacturer's recommendation, the sensor surface was subsequently loaded with the proteins up to a density of about 600 RU (1 Ru=1 pg mm⁻²). The non-reacted coupling groups were then quenched by perfusion with 1M ethanolamine pH 8.5 and the biosensor was equilibrated by perfusing the chip with running buffer (10M HEPES, 150 mM NaCl, 3.4 mM EDTA, 0.005% Tween-20, pH 7.4, referred to as HBS150). As controls two flow cells were used, one empty with no protein coupled and one coupled with an non-physiological protein partner (human Interleukin-5), which was immobilized using the same coupling chemistry and the same coupling density. For interaction measurements anti-human GDF-15 mAb-B1-23 was dissolved in HBS150 and used in six different concentrations as analyte (concentration: 0.4, 0.8, 3, 12, 49 and 98 nM). The analyte was perfused over the biosensor using the one-shot kinetics setup to avoid intermittent regeneration, all measurements were performed at 25° C. and using a flow rate of 100 μl min⁻¹. For processing the bulk face effect and unspecific binding to the sensor matrix was removed by subtracting the SPR data of the empty flow cell (flow cell 3) from all other SPR data. The resulting sensogram was analyzed using the software ProteOn Manager version 3.0. For analysis of the binding kinetics a 1:1 Langmuir-type interaction was assumed. For the association rate constant a value of 5.4±0.06×10⁵ M⁻¹s⁻¹ (k_(on)) and for the dissociation rate constant a value of 4.3+0.03×10⁻⁴ s⁻¹ (k_(off)) could be determined (values are for the interaction of anti-human GDF-15 mAb-B1-23 with GDF-15 derived from insect cell expression). The equilibrium dissociation constant was calculated using the equation K_(D)=k_(off)/k_(on) to yield a value of about 790 pM. Affinity values for the interaction of GDF-15 derived from E. coli expression and the anti-human GDF-15 mAb-B1-23 differ by less than a factor of 2, rate constants for GDF-15 derived from insect cells and E. coli deviate by about 45% and are thus within the accuracy of SPR measurements and likely do not reflect a real difference in affinity. Under the conditions used the anti-human GDF-15 mAb-B1-23 shows no binding to human interleukin-5 and thus confirms the specificity of the interaction data and the anti-human GDF-15 mAb-B1-23.

The amino acid sequence of recombinant human GDF-15 (as expressed in Baculovirus-transfected insect cells) is:

(SEQ ID No: 8) GSARNGDHCP LGPGRCCRLH TVRASLEDLG WADWVLSPRE VQVTMCIGAC PSQFRAANMH AQIKTSLHRL KPDTVPAPCC VPASYNPMVL IQKTDTGVSL QTYDDLLAKD CHCI

Thus, using surface plasmon resonance measurements, the dissociation constant (Kd) of 790 pM was determined. As a comparison: the therapeutically used antibody Rituximab has a significantly lower affinity (Kd=8 nM).

It was previously shown that mAb B1-23 inhibits cancer cell proliferation in vitro, and that mAb B1-23 inhibits growth of tumors in vivo (WO2014/049087).

Reference Example 2: mAb B1-23 Recognizes a Conformational or a Discontinuous Epitope of Human GDF-15

Epitope Mapping: Monoclonal mouse antibody GDF-15 against 13mer linear peptides derived from GDF-15

Antigen: GDF-15

(322 amino acids with linker)(SEQ ID No: 10) GSGSGSGMPGQELRTVNGSQMLLVLLVLSWLPHGGALSLAEASRASFPGPSELHSEDSRFRELRKRYEDLL TRLRANQSWEDSNTDLVPAPAVRILTPEVRLGSGGHLHLRISRAALPEGLPEASRLHRALFRLSPTASRSWDV TRPLRRQLSLARPQAPALHLRLSPPPSQSDQLLAESSSARPQLELHLRPQAARGRRRARARNGDHCPLGPG RCCRLHTVRASLEDLGWADWVLSPREVQVTMCIGACPSQFRAANMHAQIKTSLHRLKPDTVPAPCCVPASY NPMVLIQKTDTGVSLQTYDDLLAKDCHCIGSGSGSG

The protein sequence was translated into 13mer peptides with a shift of one amino acid. The C- and N-termini were elongated by a neutral GSGS linker to avoid truncated peptides (bold letters).

Control Peptides

Flag: DYKDDDDKGG (SEQ ID No:13), 78 spots; HA: YPYDVPDYAG (SEQ ID No:14), 78 spots (each array copy)

Peptide Chip Identifier

000264_01 (10/90, Ala2Asp linker)

Staining Conditions

Standard buffer: PBS, pH 7.4 +0.05% Tween 20

Blocking buffer: Rockland blocking buffer MB-070

Incubation buffer: Standard buffer with 10% Rockland blocking buffer MB-070

Primary sample: Monoclonal mouse antibody GDF-15 (1 HO): Staining in incubation buffer for 16 h at 4° C. at a dilution of 1:100 and slight shaking at 500 rpm

Secondary antibody: Goat anti-mouse IgG (H+L) IRDye680, staining in incubation buffer with a dilution of 1:5000 for 30 min at room temperature (RT)

Control antibodies: Monoclonal anti-HA (12CA5)-LL-Atto 680 (1:1000), monoclonal anti-FLAG(M2)-FluoProbes752 (1:1000); staining in incubation buffer for 1 h at RT

Scanner

Odyssey Imaging System, LI-COR Biosciences

Settings: offset: 1mm; resolution: 21 pm; intensity green/red: 7/7

Results

After 30 min pre-swelling in standard buffer and 30 min in blocking buffer, the peptide array with 10, 12 and 15mer B7H3-derived linear peptides was incubated with secondary goat anti-mouse IgG (H+L) IRDye680 antibody only at a dilution of 1:5000 for 1 h at room temperature to analyze background interactions of the secondary antibody. The PEPperCHIP® was washed 2×1 min with standard buffer, rinsed with dist. water and dried in a stream of air. Read-out was done with Odyssey Imaging System at a resolution of 21 μm and green/red intensities of 7/7: We observed a weak interaction of arginine-rich peptides (ELHLRPQAARGRR (SEQ ID No:15), LHLRPQAARGRRR (SEQ ID No:16), HLRPQAARGRRRA (SEQ ID No:17), LRPQAARGRRRAR (SEQ ID No:18), RPQAARGRRRARA (SEQ ID No:19), PQAARGRRRARAR (SEQ ID No:20) and QAARGRRRARARN (SEQ ID No:21)) that are known as frequent binders, and with the basic peptide MHAQIKTSLHRLK (SEQ ID No:22) due to ionic interactions with the charged antibody dye.

After pre-swelling for 10 min in standard buffer, the peptide microarray was incubated overnight at 4° C. with monoclonal mouse antibody GDF-15 at a dilution of 1:100. Repeated washing in standard buffer (2×1 min) was followed by incubation for 30 min with the secondary antibody at a dilution of 1:5000 at room temperature. After 2×10 sec. washing in standard buffer and short rinsing with dist. water, the PEPperCHIP® was dried in a stream of air. Read-out was done with Odyssey Imaging System at a resolution of 21 μm and green/red intensities of 7/7 before and after staining of control peptides by anti-HA and anti-FLAG(M2) antibodies.

It was shown that none of the linear 13mer peptides derived from GDF-15 interacted with monoclonal mouse antibody GDF-15 even at overregulated intensities. Staining of Flag and HA control peptides that frame the array, however, gave rise to good and homogeneous spot intensities.

Summary

The Epitope Mapping of monoclonal mouse GDF-15 antibody against GDF-15 did not reveal any linear epitope with the 13mer peptides derived from the antigen. According to this finding it is very likely that monoclonal mouse antibody GDF-15 recognizes a conformational or a discontinuous epitope with low affinity of partial epitopes. Due to the obvious absence of any GDF-15 signal above the background staining of the secondary antibody only, quantification of spot intensities with PepSlide® Analyzer and subsequent peptide annotation were omitted.

Reference Example 3: Structural Identification of Peptide Ligand Epitopes by Mass Spectrometric Epitope Excision and Epitope Extraction

The epitope of recombinant human GDF-15 which binds to the antibody B1-23 was identified by means of the epitope excision method and epitope extraction method (Suckau et al. Proc Natl Acad Sci U S A. 1990 December; 87(24): 9848-9852.; R. Stefanescu et al., Eur. J. Mass Spectrom. 13, 69-75 (2007)).

For preparation of the antibody column, the antibody B1-23 was added to NHS-activated 6-aminohexanoic acid coupled sepharose. The sepharose-coupled antibody B1-23 was then loaded into a 0.8 ml microcolumn and washed with blocking and washing buffers.

Epitope Extraction Experiment

Recombinant human GDF-15 was digested with trypsin for 2 h at 37° C. (in solution), resulting in different peptides, according to the trypsin cleavage sites in the protein. After complete digestion, the peptides were loaded on the affinity column containing the immobilized antibody B1-23. Unbound as well as potentially bound peptides of GDF-15 were used for mass spectrometry analysis. An identification of peptides by means of mass spectrometry was not possible. This was a further indicator that the binding region of GDF-15 in the immune complex B1-23 comprises a discontinuous or conformational epitope. In case of a continuous linear epitope, the digested peptides should bind its interaction partner, unless there was a trypsin cleavage site in the epitope peptide. A discontinuous or conformational epitope could be confirmed by the epitope excision method described in the following part.

Epitope Excision Experiment

The immobilized antibody B1-23 on the affinity column was then incubated with recombinant GDF-15 for 2 h. The formed immune complex on the affinity column was then incubated with trypsin for 2 h at 37° C. The cleavage resulted in different peptides derived from the recombinant GDF-15. The immobilized antibody itself is proteolytically stable. The resulting peptides of the digested GDF-15 protein, which were shielded by the antibody and thus protected from proteolytic cleavage, were eluted under acidic conditions (TFA, pH 2), collected and identified by mass spectrometry.

The epitope excision method using MS/MS identification resulted in the following peptides:

Peptide Position in sequence Mass Ion/Charge EVQVTMCIGACPSQFR 40-55 1769.91 590.50(3+) (SEQ ID No: 25) TDTGVSLQTYDDLLAKDCHCI 94-114 2310,96 771:33(3+) (SEQ ID No: 26)

The part of human GDF-15, which binds the antibody B1-23, comprises a discontinuous or conformational epitope. Mass spectrometry identified 2 peptides in the GDF-15 protein, which are responsible for the formation of the immune complex. These peptides are restricted to the positions 40-55 (EVQVTMCIGACPSQFR) and 94-114 (TDTGVSLQTYDDLLAKDCHCI) in the GDF-15 amino acid sequence. Thus, these two peptides comprise an epitope of the GDF-15 protein that binds to the antibody B1-23.

The present invention is illustrated by the following non-limiting Examples:

Example 1: In Human Melanoma Patients Who Had Received a Prior Treatment with Ipilimumab (a Monoclonal Anti-CTLA4 Antibody) and Failed to Show a Complete Response, and Who Received a Treatment with Pembrolizumab (a Monoclonal Anti-PD-1 Antibody), hGDF-15 Serum Levels Correlate With Poor Treatment Response at a Time Point of Four Months After the Start of the Treatment With Pembrolizumab

The present inventors set out to investigate whether cancer patients receiving immune checkpoint blockers could benefit from an inhibition of hGDF-15. In order to test this possibility, sera from melanoma patients, which had received a prior treatment with Ipilimumab (a monoclonal anti-CTLA4 antibody) and received a treatment with Pembrolizumab (a monoclonal anti-PD-1 antibody) in a clinical study, were analyzed for hGDF-15 serum levels. In order to investigate whether hGDF-15 influences the patients' response to immune checkpoint blockers, the obtained hGDF-15 serum levels were then correlated with the patients' responses. Sera were taken from the patients prior to the treatment with Pembrolizumab.

The study and the subsequent analyses were conducted as follows:

Inclusion Criteria of the Clinical Study

Eligible patients were aged 18 years or older and had histologically or cytologically confirmed unresectable stage III or stage IV melanoma not amenable to local therapy; confirmed disease progression within 24 weeks of the last ipilimumab dose (minimum two doses, 3 mg/kg once every 3 weeks); previous BRAF or MEK inhibitor therapy or both (if BRAFV600 mutant-positive); resolution or improvement of ipilimumab-related adverse events to grade 0-1 and prednisone dose 10 mg/day or less for at least 2 weeks before the first dose of study drug; Eastern Cooperative Oncology Group (ECOG) performance status 0 or 1; measurable disease per Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1); and values within the prespecified range for absolute neutrophil count (1500 cells per mL), platelets (100 000 cells per mL), haemoglobin (≥90 g/L), serum creatinine (<-1.5 upper limit of normal [ULN]), serum total bilirubin (1.5 ULN or direct bilirubin ≥ULN for patients with total bilirubin concentrations >1.5 ULN), aspartate and alanine aminotransferases (≥2.5 ULN or ≥5 ULN for patients with liver metastases), international normalised ratio or prothrombin time (≥1.5 ULN if not using anticoagulants), and activated partial thromboplastin time (≥1.5 ULN if not using anticoagulants). Patients had a washout period of at least 4 weeks between the last dose of the most recent therapy and the first dose of pembrolizumab. Patients with known active brain metastases or carcinomatous meningitis, active autoimmune disease, active infection requiring systemic therapy, known history of HIV infection, active hepatitis B virus or hepatitis C virus infection, a history of grade 4 ipilimumab-related adverse events or grade 3 ipilimumab-related adverse events lasting longer than 12 weeks, or previous treatment with any other anti-PD-1 or anti-PD-L1 therapy were excluded from the study.

Treatment of Patients

Human melanoma patients which met the inclusion criteria defined above had (with two exceptions) already been treated with ipilimumab (a monoclonal anti-CTLA4 antibody) and failed to show a complete response. Pembrolizumab (a monoclonal anti-PD-1 antibody).was given either at 2 mg/kg of body weight or at 10 mg/kg of body weight. As no dose-dependent differences were observed between the two treatment groups, treated patients were jointly evaluated.

Criteria for Response

Responders and Non-responders to the treatment as well as ongoing responses were classified by using the response evaluation criteria in solid tumours, version 1.1 (RECIST v1.1) (Eisenhauer et al.: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). In: Eur. J. Cancer. 45, No. 2, January 2009, pp 228-47).

Analysis of hGDF-15 Serum Levels by Enzyme-Linked Immunosorbent Assay (ELISA)

Human GDF-15 serum levels were measured by Enzyme-Linked Immunosorbent Assay (ELISA).

Buffers and Reagents:

-   -   Buffered blocking solution: 1% BSA (fraction V pH 7.0, PAA) in         PBS     -   Wash solution: PBS-Tween (0.05%)     -   Standard: human GDF-15 (stock concentration 120 μg/ml, from R&D         Systems)     -   Capture antibody: Human GDF-15 MAb (Clone 147627) from R&D         Systems, Mouse IgG2B (catalog #MAB957, from R&D Systems, stock         concentration 360 μg/ml)     -   Detection antibody: Human GDF-15 Biotinylated Affinity Purified         PAb, Goat IgG (catalog #BAF940, from R&D Systems, stock         concentration 9 μl/ml)     -   Streptavidin-HRP (Catalog #DY998, from R&D Systems)     -   Substrate solution: 10 ml 0.1 M NaOAc pH 6.0+100 μl TMB+2 μH₂O₂     -   Stop solution: 1 M H₂SO₄

Analysis Procedure: 1. Plate Preparation:

-   -   a. The capture antibody was diluted to the working concentration         of 2 μg/ml in PBS. A 96-well microplate (Nunc maxisorp®) was         immediately coated with 50 μl per well of the diluted capture         antibody excluding the outer rows (A and H). Rows A and H were         filled with buffer to prevent evaporation of the samples during         the experiment. The plate was gently tapped to ensure that the         bottom of each well was thoroughly covered. The plate was placed         in a humid chamber and incubated overnight at room temperature         (RT).     -   b. Each well was aspirated and washed three times with PBS-Tween         (0.05%).     -   c. 150 μl of blocking solution was added to each well, followed         by incubation at RT for 1 hour.     -   d. Each well was aspirated and washed three times with PBS-Tween         (0.05%).

2. Assay Procedure:

-   -   a. Standards were prepared. GDF-15 was diluted in buffered         blocking solution to a final concentration of 1 ng/ml (4.17 μl         GDF+496 μl buffered blocking solution). 1:2 serial dilutions         were made.     -   b. Duplicate samples 1:20 (6 μl+114 μl buffered blocking         solution) were prepared.     -   c. 50 μl of diluted samples or standards were added per well,         followed by incubation for 1 hour at RT.

1 2 3 4 5 6 7 8 9 10 11 12 A 0 0 0 0 0 0 0 0 0 0 0 0 B s1  s2  . . . s12 C s1  s2  . . . s12 D s13 s14 . . . s24 E s13 s14 . . . s24 F St and ard dil uti on s G se rial H 0 0 0 0 0 0 0 0 0 0 0 0

-   -   a. Each well was aspirated and washed three times with PBS-Tween         (0.05%).     -   b. The detection antibody was diluted to a final concentration         of 50 ng/ml (56 μl+10 ml blocking buffer). 50 μl of the diluted         detection antibody was added to each well, followed by         incubation for 1 hour at RT.     -   c. Each well was aspirated and washed three times with PBS-Tween         (0.05%).     -   d. Streptavidin-HRP was diluted 1:200 (50 μl+10 ml blocking         buffer). 50 μL of the working dilution of Streptavidin-HRP was         added to each well, followed by incubation for 20 min at RT.     -   e. Each well was aspirated and washed three times with PBS-Tween         (0.05%).     -   f. The substrate solution was prepared. 50 μL of substrate         solution was added to each well, followed by incubation for 20         min at RT.     -   g. 50 μL of stop solution was added to each well.     -   h. The optical density of each well was determined immediately,         using a microplate reader set to 450 nm.

3. Calculation of GDF-15 Serum Titer:

-   -   a. Each sample/GDF-15 standard dilution was applied in         duplicate. To determine GDF-15 titer, the average of the         duplicates was calculated and the background (sample without         GDF-15) subtracted.     -   b. To create a standard curve, values from the linear range were         plotted on an X-Y-diagram (X axis: GDF-15 concentration, Y axis:         OD450), and a linear curve fit was applied. GDF-15 serum titer         of the test samples was calculated by interpolating from the         OD450 values of the standard dilutions with known concentration.     -   c. To calculate the final GDF-15 concentration of the samples,         the distinct dilution factor was considered. Samples yielding OD         values below or above the standard range were re-analyzed at         appropriate dilutions.

Comparison of hGDF-15 Serum Levels with Patient Data

Next, the measured hGDF-15 serum levels were compared with patient response data obtained from the study.

FIG. 1 shows the GDF-15 serum levels for responders and non-responders to the treatment regimen. All serum samples had been obtained prior to treatment with the anti PD-1 antibody. As can be seen from the Figure, most of the non-responders have higher GDF-15 serum levels than all of the responders.

This result is also reflected in FIG. 2, which shows the numbers of responders and non-responders in the patients having hGDF-15 serum levels of <1.8 ng/ml, 1.8-4.2 ng/ml, and >4.2 ng/ml, respectively.

These findings suggested that high GDF-15 levels are related to a poor treatment response. Therefore, these findings were tested for their statistical significance:

Statistical Correlation of hGDF-15 Serum Levels with Patient Data Data:

The data analysis was based on a data file containing data from samples from 35 patients containing the columns (variables) Sample designation, GDF-15 (ng/ml), responder/non-responder, days (to death or censoring), and Ongoing (an index variable for ongoing life). The responder/non-responder classification of these data was made at a time point of four months after the start of the treatment with pembrolizumab. As some serum samples had only been obtained shortly before the analysis, response could only by assessed in 29 patients. One partial responder (>30% reduction in tumor size) was rated as responder. For LDH determination, 4 samples had to be excluded due to hemolysis.

Outcome Variables (Endpoints):

-   -   a. Overall survival (time to death). This endpoint is composed         of the event indicator for death (1=dead/0=alive), which was         derived from the data file, and the time to death or censoring         (last time the patient was known to be alive), corresponding to         the variable “days”.     -   b. Response to treatment, e.g. whether a patient was a responder         or not (coded as 1=responder, 0=nonresponder). Partial         responders were considered as responders.

Prior Sample GDF-15 responder/ Days since Ipilimumab Ongoing designation (ng/ml) LDH[U/I] non-responder anti PD-1 treatment Response HG12.950 2.010 398 NR 72 x HG13.1002 0.479 340 R 538 x HG13.1012 12.010 3734  NR 71 x HG13.1067 9.173 591 NR 83 x HG13.1069 4.635 2419  NR 53 x HG13.1099 1.285 370 R 693 x x HG13.1202 1.641 480 R 575 x HG13.1341 4.595 1930  NR 15 x HG13.1377 0.539 388 R 269 x HG13.1419 0.914 317 R 617 x HG13.1432 1.195 269 R 611 x x HG13.1458 0.433 453 R 605 x x HG13.1557 4.045 564 R 293 x HG13.1587 0.345 371 R 186 x HG13.1663 1.320 hemolytic R 176 x HG13.516 0.641 342 R 264 x HG13.578 2.841 1143  R 266 x HG13.596 1.085 hemolytic R 772 x x HG13.757 3.310 hemolytic NR 117 x HG13.811 4.029 763 R 596 x x HG14.1080 5.979 1359  NR 43 x HG14.1108 0.979 555 R 206 x x HG14.1147 2.084 227 R 154 x x HG14.1159 2.150 333 R 227 x x HG14.161 0.889 343 108 x x HG14.557 2.014 368 R 317 x x HG14.707 2.783 442 NR 71 x HG14.853 0.846 343 NR 71 x HG14.885 0.874 hemolytic PR 63 x HG15.299 0.412 354 86 x x HG15.47 1.465 475 80 x x HG15.49 3.912 631 93 x x HG15.546 0.358 hemolytic 23 x x HG15.560 2.389 768 21 x x HG15.59 8.122 588 NR 23 x

Data Analysis:

Overall survival was analysed by Cox proportional hazard survival models. One model was fitted with GDF-15 (ng/ml) as continuous predictor and another model with a grouping variable based on GDF-15 as categorical predictor (groups were: <1.8 ng/ml, 1.8-4.2 ng/ml, >4.2 ng/ml of GDF-15). Altogether, survival data were available from 35 patients.

Response to treatment (binary variable) was analysed by Generalised Linear Models (GLMs) with binomial error distribution and logit link function (logistic regression). For the response to treatment as assessed by RECIST1.1 criteria after 4 months a model was fitted with GDF-15 (ng/ml) as continuous predictor. Because no patients responded in the group with GDF-15 >4.2 ng/ml, the odds ratio estimate for this group vs. the group with GDF-15 <1.8 ng/ml would be very big, with a very wide confidence interval. Instead of fitting another model with the grouping variable based on GDF-15 as categorical predictor, a chi-squared (x2) test was used to compare the groups (testing the equality of the proportion of responders). Because the number of responders/non-responders was sometimes quite small (<5), a sensitivity analysis using Fisher's exact test was done in addition. Patients who had only received anti PD-1 within the last 4 months could not yet be classified as responders or non-responders. Hence, only 29 patients could be evaluated for response to therapy.

Data analysis was performed using the statistical software package R (R Core Team, 2014, version 3.1.0).

Results:

Tables 1-2 show the results from the models with GDF-15 as continuous predictor. The hazard for death significantly increased for higher concentrations of GDF-15 (HR>1, Table 1) whereas the probability of response to treatment significantly decreased, as indicated by the odds ratio (OR) (OR<1, Table 2). FIG. 3 shows the corresponding data on responders/non-responders as well as the probability of response to treatment predicted by the model.

Table 3 shows the result from the Cox proportional hazards model with the group based on GDF-15 as categorical predictor. The group with GDF-15 <1.8 ng/ml is used as reference group (not shown in the Table). The two hazard ratios in Table 3 represent the comparison of the group with GDF-15 between 1.8 and 4.2 and the group with GDF-15 >4.2 with the reference group. The hazard for death is increased in both of these groups (compared to the reference group), but to a larger extent in the group with GDF-15 >4.2. FIG. 4A shows the Kaplan-Meier curves for survival in the three groups.

The proportion of responders differed significantly between the groups (responder 1: x²df=2^(=16.04), P=0.0003). This result was confirmed by the results of Fisher's exact test (P=0.0003). The numbers of deaths and responders per group are given in Table 4. Moreover, Table 5 shows some descriptive statistics of the GDF-15 for each group.

TABLE 1 HR 95% CI z p GDF-15 1.27 [1.10, 1.47] 3.27 0.00109

Table 1 shows the Hazard ratio (HR) estimates from the Cox proportional hazards model with overall survival (time to death) as outcome variable and GDF-15 as continuous predictor. The analysis included samples from 35 patients.

TABLE 2 Estimate (OR) 95% CI z p (Intercept) 25.281 [4.219, 364.950] 2.94 0.00324 GDF-15 0.389 [0.159, 0.698] −2.54 0.01120

Table 2 shows the Odds ratio (OR) estimates from the Generalized Linear Model with response to treatment (responder 1) as outcome variable and GDF-15 as continuous predictor. The analysis included samples from 29 patients.

TABLE 3 HR 95% CI z p GDF-15-group(1.8, 4.2] 1.54 [0.48, 4.92] 0.73 0.466 GDF-15-group(4.2, 13] 21.52 [5.20, 89.06] 4.24 <0.001

Table 3 shows Hazard ratio (HR) estimates from the Cox proportional hazards model with overall survival (time to death) as outcome variable and the group based on GDF-15 as categorical predictor. The analysis included samples from 35 patients.

TABLE 4 Variable Levels n_([0, 1.8]) %_([0, 1.8]) n_((1.8, 4.2]) %_((1.8, 4.2]) n_((4.2, 13]) %_((4.2, 13]) n_(all) %_(all) death 0 11 61.1 6 54.5 0 0.0 17 48.6 1 7 38.9 5 45.5 6 100.0 18 51.4 all 18 100.0 11 100.0 6 100.0 35 100.0 responder1 0 1 7.1 3 33.3 6 100.0 10 34.5 1 13 92.9 6 66.7 0 0.0 19 65.5 all 14 100.0 9 100.0 6 100.0 29 100.0

Table 4 shows the number of deaths and responders (“responder1”) in the three groups defined by the GDF-15 (<1.8, 1.8-4.2, >4.2 ng/ml).

TABLE 5 Table 5: The continuous predictor variable GDF-15 (ng/ml) in the three groups defined by the GDF-15 (<1.8, 1.8- 4.2, >4.2 ng/ml). The number of patients (n), the median (x{tilde over ( )}), the mean (x ), the standard deviation (s), the minimum (Min), and the maximum (Max) are shown. Variable Levels n {tilde over (x)} x s Min Max GDF-15  [0, 1.8] 18 0.9 0.9 0.4 0.3 1.6 (1.8, 4.2] 11 2.8 2.9 0.8 2.0 4.0 (4.2, 13]  6 7.1 7.4 2.9 4.6 12.0 all 35 1.6 2.6 2.7 0.3 12.0

Lactate dehydrogenase (LDH) is considered to be a prognostically relevant marker for solid tumors. This has recently been confirmed by a comprehensive meta-analysis based on a large pool of clinical studies (31,857 patients). A consistent effect of an elevated LDH on OS (HR=1.48, 95% Cl=1.43 to 1.53) was found across all disease subgroups and stages. In addition, there was a trend toward a stronger prognostic value of LDH in metastatic disease compared with non-metastatic disease, which was thought to reflect greater tumor burden. While the exact mechanism remains unknown and may also be related to hypoxia and metabolic reprogramming via a Warburg effect, LDH may be interpreted as reflecting high tumor burden or tumor aggressiveness (Zhang, J., Yao, Y.-H., Li, B.-G., Yang, Q., Zhang, P.-Y., and Wang, H.-T. (2015). Prognostic value of pretreatment serum lactate dehydrogenase level in patients with solid tumors: a systematic review and meta-analysis. Scientific Reports 5, 9800). As serum LDH levels have been incorporated into the staging scheme for melanoma, this parameter is routinely measured during clinical diagnostics by the university reference laboratory.

TABLE 6 Table 6: GDF-15 and LDH in responders vs. non-responders GDF-15 (ng/ml) LDH (U/l) non- non- Responder responder Responder responder (n = 19) (n = 10) (n = 9) (n = 16) Median 1.2 4.6 371 591 Mean 1.7 5.6 455 1312 Standard deviation 1.2 3.6 218 1108 ttest (2-sided, type 3) 0.012 0.061

LDH determination failed in 4 blood samples due to hemolysis.

Table 7 is analogue to Table 2, except that LDH was used as continuous predictor of response to treatment (responder1) instead of GDF-15. The probability of response to treatment marginally significantly decreased with increasing values of LDH (OR<1, p<0.1). FIG. 5 shows the corresponding data on responders/non-responders as well as the probability of response to treatment predicted by the model.

In order to determine, whether GDF-15 is the better predictor of response to treatment (responder1) than LDH, two additional models were fitted: a model containing both markers as predictors (which automatically only includes patients with measurements on both markers), and a model with GDF-15 as the only predictor but also only using the patients with a measurement of LDH. Then, Akaike's information criterion (AIC) was calculated for all three models (Table 8). A smaller AIC indicates a more efficient model. In fact, the AIC of the model with GDF-15 was smaller than the AIC of the model with LDH as predictor. The model with GDF-15 only even has a smaller AIC than the model with both predictors, indicating that LDH as an additional predictor does not improve the model. Of course, the model with both predictors cannot explain the response to treatment worse, but as a measure of “model efficiency”, the AIC penalizes models with predictors that do not improve the model considerably and favours simpler models. An alternative model comparison was done by analysis of deviance (similar to analysis of variance but for generalized linear models), i.e., comparing the difference in the deviance explained between a the more complex model with both predictors and both of the simpler models with only one of the predictors (corresponding to a reduction of the model by either LDH or GDF-15). Removing GDF-15 from the more complex model resulted in a significant reduction in the deviance explained (P=0.02) whereas removing LDH did not (P=0.41).

TABLE 7 Table 7: Odds ratio (OR) estimates from the Generalized Linear Model with response to treatment (responder 1, as defined in file A) as outcome variable and LDH as continuous predictor. The analysis included samples from 25 patients. Estimate (OR) 95% CI z p (Intercept) 9.741 [2.055, 89.308] 2.44 0.0146 LDH 0.997 [0.994, 0.999] −1.79 0.0727

TABLE 8 Table 8: Model comparison based on Akaike's information criterion (AIC) of which smaller values indicate a more efficient model, df: degrees of freedom. All models included samples from 25 patients. df AIC Model with LDH and GDF-15 3.00 25.10 Model with LDH only 2.00 28.55 Model with GDF-15 only 2.00 23.77

FIG. 5A shows the probability of response to treatment (responder 1) as predicted by the Generalized Linear Model model using LDH as continuous predictor. Circles show the data, the curve shows the model. The vertical line indicates the LDH concentration where the probability of treatment response is 0.5. The patient cohort was identical. However, reliable determination of LDH levels failed in four patients due to hemolysis. FIG. 5B shows a graphical representation of responders and non-responders and their respective hGDF-15 and LDH levels. When cut-off values are selected to cover all responders, testing based on GDF-15 allows for identification of 6 (out of 9) non-responders whereas analyses based on LDH levels can only discriminate 4 (out of 9) non-responders. For LDH testing, 4 hemolytic samples had to be excluded which causes loss of data.

Thus, a prediction of a clinical outcome based on hGDF-15 levels according to the invention includes the following advantages over the diagnostic standard marker LDH for solid tumors:

-   -   There is a stronger inverse statistical correlation between         hGDF-15 levels and a positive clinical outcome than between LDH         levels and a positive clinical outcome, and hence, hGDF-15         levels are superior for a prediction as compared to LDH levels.         Moreover, as reflected by Akaike's information criterion         indicated above, hGDF-15 levels alone are even a better         predictor than hGDF-15 levels in combination with LDH levels.     -   The hGDF-15 measurement is less sensitive to hemolysis than the         LDH measurement and therefore advantageous in clinical practice.     -   hGDF-15 levels allow to discriminate a higher number of         non-responders than LDH levels.

These advantages are particularly noteworthy since LDH is currently considered as the best available clinical marker for solid tumors.

Summary

Taken together, the above statistical results of Example 1 showed that the probability of a response to the treatment significantly decreases with increasing hGDF-15 levels in the patient sera. For instance, the odds ratio of 0.389 shown in Table 2 indicates that if hGDF-15 serum levels are increased by 1 ng/ml, the probability of a response to the treatment decreases to the 0.389-fold value of the original value, i.e. it decreases by about 60%. If hGDF-15 serum levels are increased by 2 ng/ml, the probability of a response to the treatment decreases to the 0.389×0.389-fold=0.151-fold value of the original value, i.e. it decreases by about 85%.

Similarly, the hazard ratio of 1.27 shown in Table 1 indicates that if hGDF-15 serum levels are increased by 1 ng/ml, the patients' probability to die increases by a factor of 1.27.

The results of Example 1 indicate that there is a strong inverse correlation between the serum levels of hGDF-15 and the probability of a positive clinical outcome of e.g. anti PD-1 based immunotherapy in the patients, including patient response and patient survival. Thus, according to the invention, levels of hGDF-15 in blood samples from patients can advantageously be used to predict the probability of a response of patients to a treatment with immune checkpoint blockers like anti PD-1.

While the present Example shows results for melanoma as an example of a solid tumor, hGDF-15 expression is not limited to melanoma but also present in numerous other solid cancers. Likewise, it is known that solid tumors other than melanoma can also be treated with immune checkpoint blockers. Thus, according to the invention, levels of hGDF-15 in blood samples from patients can advantageously be used to predict the probability of a response of patients to a treatment with immune checkpoint blockers not only in melanoma, but in all of the solid cancers referred to herein.

Example 2: GDF-15 Levels Inversely Correlate With CD8⁺ Tumor Infiltrating Lymphocytes (TILs) in Metastases of Different Tumor Entities

In order to identify a mechanism of hGDF-15 that contributes to the negative effect of hGDF-15 on the patients' responses, brain metastases from different solid tumors were analyzed for the expression of hGDF-15 and for the presence of cells of the immune system:

Tissue Specimen and Tissue Processing

Formalin-fixed and paraffin-embedded (FFPE) tissue from archived brain metastases was analyzed, which was collected and processed as tissue micro arrays (TMAs). All specimens were obtained either from the UCT tumor bank (Goethe-University, Frankfurt am Main, Germany, member of the German Cancer Consortium (DKTK), Heidelberg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany) or from the cancer registry tumor bank “”Blut-und Gewebebank zur Erforschung des malignen Melanoms” (Department of Dermato-oncolgy, University Hospital Tübingen, Germany). Approval for this study was conferred by two independent ethical committees (Ethics committee UCT Frankfurt/Goethe University Frankfurt am Main, Germany: project numbers: GS 4/09; SNO_01-12; Ethics committee University of Tübingen project number: 408/2013BO2). In total, 190 patients with brain metastases were investigated including: melanoma (n=98), NSCLC (n=33), breast carcinoma (n=18), RCC (n=10), SCLC (n=7), colorectal carcinoma (n=7), carcinomas which were not otherwise specified (carcinoma NOS n=11) and specimens of rare tumors summarized as others (n=6). Survival data of 155 patients (survival time after tumor resection) were collected, additionally the number of brain metastases in 169 patients and brain metastases size in a subcohort of 55 melanoma patients was analyzed.

Immunohistochemistry

Immunohistochemistry for all antibodies was performed using 3 pm thick slides and standard protocols on the automated IHC staining system Discovery XT (RocheNentana, Tucson, Ariz., USA). The following antibodies were used: anti GDF-15 (HPA011191, dilution 1:50, Sigma/Atlas, protocol #730), CD3 (clone A0452, dilution 1:500, DAKO, Glostrup, Denmark), CD8 (clone C8/144B, dilution 1:100, DAKO, Glostrup, Denmark), PD-1 (clone NAT105; dilution 1:50; Abcam, Cambridge, United Kingdom), PD-L1 (E1L3N; dilution 1:200; Cell Signaling, Boston, U.S.A.), FOXP3 (clone 236A/E7; dilution 1:100; eBioscience, San Diego, U.S.A.). Slides were counterstained with hematoxylin and mounted.

Statistical Analyses

All samples were scored according to the frequency of positive cells related to all cells (as percentage) on the stained TMA core. For hGDF-15 expression, a score as previously described in detail [21,22] was used: frequency 0-1% score 0; 1-10% score 1; 10-25% score 2; 25-50% score 3; >50% score 4; additionally the frequency score was multiplied with the intensity of staining (1 weak staining, 2 moderate staining, 3 strong staining), finally resulting in the ordinal scaled hGDF-15 score (0, 1, 2, 3, 4, 6, 8, 9, 12). Ordinal scaled variables were compared with non-parametric Wilcoxon/Kruskal-Wallis-Test and Dunn's method to correct for multiple testing. For continuous variables, means were compared between different brain metastases entities using ANOVA, followed by Tukey-Kramer HSD post-hoc Test. For correlation analyses of brain metastases size and marker expression, a linear fit was performed followed by ANOVA, in case of ordinal scaled variables, Spearman's rho correlation analysis was used. A significance level of p<0.05 was set for all statistical analyses.

All statistical analyses were performed using JMP8 and JMP11 (SAS, Cary, U.S.A.), additional graphics were created with Prism 6 (GraphPad Software, La Jolla, U.S.A.).

Results

FIG. 6 shows exemplary tissue sections from melanoma brain metastases having high no (upper panel) or high (lower panel) GDF-15 immunoreactivity, which were stained by immunohistochemistry for GDF-15 and for the T-cell marker proteins CD3 and CD8, respectively, as indicated in the Figure. In the section with no GDF-15 expression, the numerous infiltrating immune cells are seen as dark spots. In the picture showing the metastasis expressing high levels of GDF-15, the scarce infiltrating immune cells are depicted by arrows (CD3 and CD8-positive cells are indicated by arrows). As can be seen from the Figure, it was surprisingly found that in the tissue section with high hGDF-15 immunoreactivity (lower panel), the number of CD3⁺ and CD8⁺ cells was strongly reduced compared to the tissue section with no hGDF-15 immunoreactivity (upper panel). Of note, other markers stained like PD-L1, PD-1 all showed a positive correlation with the number of tumor-infiltrating CD3+ and CD8⁺ T cells.

Therefore, it was next analyzed whether there exists an inverse correlation between hGDF-15 levels and the percentage of CD3⁺ T cells across different melanoma brain metastases. FIG. 7A shows a plot of the percentage of CD3⁺ cells against the GDF-15 score (obtained as described above in the “statistical analyses” section). As indicated in FIG. 7A, there was a statistically significant inverse correlation between the percentage of CD3⁺ cells and the GDF-15 score (p=0.0015).

Similarly, it was also analyzed whether there exists an inverse correlation between hGDF-15 levels and the percentage of CD8⁺ T cells across different melanoma brain metastases. FIG. 7B shows a plot of the percentage of CD8⁺ cells against the GDF-15 score (obtained as described above in the “statistical analyses” section). As indicated in FIG. 7B, there was a statistically significant inverse correlation between the percentage of CD8⁺ cells and the GDF-15 score (p=0.0038).

Correlating GDF-15 with FOXP3, in contrast, gave no statistically significant result according to Spearman's rank correlation coefficient (rho) test (p=0.8495 across different tumor entities; p=0.2455 when assessing only melanoma metastases).

Finally, it was also analyzed whether there exists an inverse correlation between hGDF-15 levels and the percentages of CD8⁺ and CD3⁺ T cells across brain metastases from different tumor entities. FIG. 8 shows a plot of the GDF-15 score against the percentage of CD8⁺ and CD3⁺ T cells, respectively, in 168 (for CD3) or, respectively, 169 (for CD8) brain metastases from different tumor entities (melanoma, CRC, RCC, breast cancer, NSCLC and SCLC). The plot was obtained as described above in the “statistical analyses” section. As indicated in FIG. 8, there was a statistically significant inverse correlation between the percentage of CD8⁺ cells and the GDF-15 score (p=0.0311) as well as a statistically significant inverse correlation between the percentage of CD3⁺ cells and the GDF-15 score (p=0.0093). Other markers (PD-L1, PD-1, FOXP3) again showed positive correlations with CD3 and CD8 T cell infiltration.

Summary

The above results show that there is not only an inverse correlation of hGDF-15 with the percentage of T-cells expressing the general T-cell marker protein CD3 in the metastases, but also an inverse correlation with the percentage of CD8⁺ T lymphocytes in the metastases. This is noteworthy, because the presence of CD8⁺ T lymphocytes was previously shown to be specifically required for tumor regression after immune checkpoint inhibition with an anti-PD-1 antibody (Tumeh et al., Nature. 2014 Nov. 27; 515(7528):568-71.).

Thus, according to the invention, a preferred but not limiting explanation for the inverse correlation of hGDF-15 levels and a favorable clinical outcome (e.g. patient survival or the presence of a treatment response) is that hGDF-15 decreases the percentage of CD8⁺ T lymphocytes in solid tumors including tumor metastases, thereby decreasing the probability of a favorable clinical outcome (e.g. patient survival or the presence of a treatment response). Since this correlation is observed across various solid cancer entities, the present invention is not limited to particular solid cancers such as melanoma.

Thus, the invention can be applied to all of the solid tumors as referred to in the preferred embodiments.

Example 3: GDF-15 Decreases Adhesion of T Cells to Endothelial Cells

The inventors next set out to determine how hGDF-15 affects the percentage of T cells in the solid tumors.

A step which is required for the invasion of T cells from the blood stream into the tumor tissue is that the T cells must first adhere to the endothelium before they can enter the tumor. In order to simulate this step and to assess whether this step could be affected by hGDF-15, the inventors used a model system which measures the adhesion of T cells to Human Umbilical Vein Endothelial Cells (HUVEC):

T Cell Flow/Adhesion Experiment (on HUVEC) Day 1:

-   -   a. μ-slides VI 0.4 (ibidi GmbH, Germany) were coated with         fibronectin (100 μg/mL): 30 μL per loading port. They were         incubated for 1 h at 37° C. (or a pre-coated slide was used).     -   b. Fibronectin was aspirated, followed by a wash with HUVEC         medium.     -   c. HUVECs were trypsinized from a 6-well plate (count: 2×10⁵/mL         (2 mL total))     -   d. They were washed and diluted to 1×10⁶ cells/mL     -   e. 30 μL of HUVECs were applied in loading ports of the p-slide         VI and checked under a microscope     -   f. The p-slide VI was covered with a lid and incubated at 37°         C., 5% CO₂i

Day 2:

-   -   a. HUVECs were activated with TNFα (10 ng/mL) and IFNγ (10         ng/mL) in channels 2-5 (see table below): All media were         aspirated from the channels and replaced with         cytokine-containing pre-warmed media.

Day 3:

-   -   a. T cells were isolated (negative isolation of pan T cells).     -   b. T cells were pre-incubated in well 1 (1×10⁶ cells/mL) with or         without GDF-15 (100 ng/mL) for 1 h.     -   c. HUVECs were pre-incubated in channels 4 and 5 with GDF-15         (100 ng/mL) for 1 h: All medium in loading ports was aspirated,         and both loading ports were filled with pre-warmed medium         containing GDF15.     -   d. A stage top incubator next to the microscope was pre-warmed,         and a gas-mix was connected (5% CO₂, 16% O₂, 79% N₂).     -   e. 3×50 mL syringes were prepared:         -   i. T cells (1×10⁶ cells/mL): 1 mL         -   ii. T cells GDF15 (1×10⁶ cells/mL): 1 mL         -   iii. Medium     -   f. Syringe 1 was connected to channel 1 (see table below) and         the flow was started (0.5 dyn/cm²: 0.38 mL/min=22.8 mL/h).     -   g. T cells were flowed for 3 min and in the meantime, 10 fields         of view were predefined on the microscope.     -   h. Each field of view was video-imaged for 5 s.     -   i. The remaining channels were assessed in analogy to channel 1         (f-h) with the T cell samples as indicated in the table below.

Channel # endothelial cells T cells in flow comments 1 HUVEC unstimulated T cells [negative control] 2 HUVEC T cells [positive control] stimulated 3 HUVEC T cells stimulated GDF-15 4 HUVEC T cells stimulated GDF-15 5 HUVEC T cells Stimulated GDF-15 GDF-15 Recombinant GDF-15 was obtained from Invigate GmbH, Jena, Germany.

Statistical Analysis

All data were compared using Mann-Whitney test for testing of non-normally distributed data. Values of p<0.05 were considered to be statistically significant.

Results

The results of the experiment are shown in FIG. 9. This Figure shows analyses of several adhesion parameters, namely

-   -   a. the number of rolling T cells per field of view per second         (9A; the data were obtained from channel #3 (“GDF-15”) and         channel #2 (“control”)), which reflects a form of moderate         adhesion of the T cells to the endothelial cells,     -   b. the rolling speed of the T cells (measured in pixels per 0.2         seconds) (9B; the data were obtained from channel #3 (“GDF-15”)         and channel #2 (“control”)), which increases with decreasing         adhesion between the T cells and the endothelial cells, and     -   c. the number of adhering cells per field of view (9C; the data         were obtained from channel #3 (“GDF-15”) and channel #2         (“control”); and 9D).

As can be seen from FIG. 9C, it was found that treatment of the T cells with hGDF-15 significantly decreases the adhesion to the endothelial cells, as reflected in the number of adhering cells per field of view. Similar results were obtained when analyzing adhesion by counting the numbers of rolling T cells (FIG. 9A). Furthermore, and consistent with the above results, it was found that treatment of the T cells with hGDF-15 significantly increases the rolling speed, indicating a decrease in the interaction time between the T cells and the endothelial cells, and also indicating a reduced adhesion between the T cells and the endothelial cells (FIG. 9B).

The inventors next analyzed which cells were targeted by hGDF-15 (FIG. 9D). In the sample where only HUVEC were treated with hGDF-15, a moderate decrease in the adhesion of the T cells to the endothelial cells (HUVECs) was observed. In contrast, a strong decrease in the adhesion of the T cells to the endothelial cells (HUVECs) was observed when either only the T cells were treated with hGDF-15, or when both the T cells and the endothelial cells (HUVECs) were treated with hGDF-15. These results indicate that hGDF-15 acts both on the T cells and on the endothelial cells, but they also indicate that the main adhesion effect of hGDF-15 is an effect on the T cells.

Next the inventors tested whether effects of hGDF-15, which is secreted by tumor cells, on T-cell adhesion could be inhibited with an hGDF-15 inhibitor. In order to test this, the inventors used an hGDF-15-secreting melanoma cell line, UACC257:

T Cell Flow/Adhesion Experiment (on HUVEC) in the Presence or Absence of GDF-15 in Tumor Cell Supernatant Day 1:

-   -   a. One p-slide VI 0.4 (ibidi GmbH, Germany; from now on referred         to as p-slide) were coated with fibronectin (100 μg/mL): 30 μL         per loading port. They were incubated for 1 h at 37° C. (or a         pre-coated slide was used).     -   b. Fibronectin was aspirated, followed by a wash with HUVEC         medium.     -   c. HUVECs were trypsinized from a 6-well plate (count: 2×10⁵/mL         (2 mL total))     -   d. They were washed and diluted to 1×10⁶ cells/mL     -   e. 30 μL of HUVECs were applied in loading ports of the p-slide         and checked under a microscope     -   f. The p-slide was covered with a lid and incubated at 37° C.,         5% CO₂.

Day 2:

-   -   a. HUVECs were activated with TNFα (10 ng/mL) and IFNγ (10         ng/mL) in channels 2-5 of the p-slide (see table below): All         media were aspirated from the channes and replaced with         cytokine-containing pre-warmed media.

Day 3:

-   -   a. T cells were isolated (negative isolation of pan T cells).     -   b. In parallel 24 wells of an 96-well ELISA-plate (Nunc         maxisorb) were coated with 200 μL anti-GDF-15 (10 μg/mL diluted         in PBS), incubated for 45 min and then washed with PBS.     -   c. To deplete supernatant from the melanoma cell line UACC257         which secrets GDF-15 (data not shown) from GDF-15 the         supernatant was incubated in wells of the ELISA-plate (see b.)         that were pre-coated with anti-GDF-15.     -   d. As a control supernatant of the melanoma cell line UACC257         was incubated in wells of the ELISA-plate (see b.) that were not         pre-coated with anti-GDF-15.     -   e. T cells were pre-incubated in a 12-well cell culture plate         (1×10⁶ cells/mL) with GDF-15 (100 ng/mL), without GDF-15, in         supernatant of the melanoma cell line UACC257 depleted from         GDF-15 (see c.) or in supernatant of the melanoma cell line         UACC257 containing GDF-15 (see d.) for 1 h.     -   f. A stage top incubator next to the microscope was pre-warmed,         and a gas-mix was connected (5% CO₂, 16% O₂, 79% N₂).     -   g. 4×2 mL tubes of a microfluidic flow system were prepared:         -   i. T cells (1×10⁶ cells/mL): 1 mL         -   ii. T cells GDF15 (1×10⁶ cells/mL): 1 mL         -   iii. T cells UACC 257 (containing GDF-15)         -   iv. T cells UACC 257 depleted from GDF-15     -   h. Tube 1 was connected to channel 1 (see table below) and the         flow was started (0.4 mL/min =24 mL/h).     -   i. T cells were flowed for 3 min and in the meantime, 5 fields         of view were predefined on the microscope.     -   j. Each field of view was video-imaged for 5 s.     -   k. The remaining channels were assessed in analogy to channel 1         (f-h) with the T cell samples as indicated in the table below.

channel # endothelial cells T cells in flow comments 1 HUVEC unstimulated T cells [negative control] 2 HUVEC stimulated T cells [positive control] 3 HUVEC stimulated T cells GDF-15 4 HUVEC stimulated T cells UACC 257 5 HUVEC T cells stimulated UACC 257 depleted from GDF-15 with anti GDF-15

Recombinant GDF-15 was obtained from Invigate GmbH, Jena, Germany.

Results

The results of the experiment are shown in FIG. 10. This Figure shows analyses of the number of rolling T cells per field of view per second. The data were obtained from channel #1 (control T cells on unstimulated HUVEC as “neg. control”), channel #2 (control T cells on stimulated HUVEC as “pos. control”), channel #3 (“GDF-15”) channel #4 (“UACC 257”: T cells cultured in the supernatant of UACC 257 melanoma cells containing secreted GDF-15) and channel #5 (“UACC257 +anti-hGDF-15”: T cells cultured in the supernatant of UACC 257 melanoma cells depleted from secreted GDF-15 with anti hGDF-15 B1-23)

In comparison to T cells flown over unstimulated HUVEC (“neg. control”; median=28 rolling cells per field of view per second) flowing of T cells over stimulated HUVEC (“pos. control”) increased the number of rolling cells per field of view per second (median=46). Treatment of the T cells with hGDF-15 substantially decreases the number of rolling cells per field of view per second (median=29). Also, pre-incubation of the T cells with supernatant of the melanoma cell line UACC257 that secrets GDF-15 substantially decreases the number of rolling cells per field of view per second (median=36) as compared to T cells flowing over stimulated HUVEC (“pos. control”). I contrast to this, pre-incubation of the T cells with supernatant of the melanoma cell line UACC257 depleted from secreted GDF-15 with anti GDF-15 resulted in numbers of rolling cells per field of view per second (median=45) that were comparable to T cells flowing over stimulated HUVEC (“pos. control”).

Summary

This example shows that hGDF-15, including GDF-15 secreted by tumor cells, decreases adhesion of T cells to endothelial cells. Since the entry of CD8⁺ T cells into solid cancers and the presence of these CD8⁺ T cells in the solid cancers is particularly advantageous for therapeutic approaches using immune checkpoint blockers, levels of hGDF-15 can be used to predict the probability of a response to treatments of these cancer patients with immune checkpoint blockers.

Example 4: GDF-15 Serum Levels Define Survival of Melanoma Patients Treated with Anti PD-1

The study in this Example was performed in order to further validate the results obtained in the study of Example 1, e.g. the finding that hGDF-15 influences the patients' response to immune checkpoint blockers, by an additional independent study.

The following terms were used in connection with this study:

-   -   “Censored”=The patient was removed from the study cohort when no         further follow-up data were available.     -   “Event”=The patient had died.     -   “Survival”=The patient was alive at follow-up.

Patients from the Department of Dermatology, University of Tubingen, Germany, with histologically confirmed melanoma were identified in the Central Malignant Melanoma Registry (CMMR) database which prospectively records patients from more than 60 dermatological centers in Germany. 99 patients, with (a) archived serum samples, (b) available follow-up data, (c) history or presence of loco regional or distant metastasis at the time point of blood draw and (d) experimental treatment with anti PD-1 antibody were selected. The aims and methods of data collection by the CMMR have previously been published in detail (Lasithiotakis, K G et al., Cancer/107/1331-9. 2006). Data obtained for each patient included age, gender, the date of the last follow-up, and the date and cause of death, if applicable. All patients had given written informed consent to have clinical data recorded by the CMMR registry. The institutional ethics committee Tubingen has approved the study (ethic vote 125/2015BO2). Eligible patients were aged 18 years or older and had histologically or cytologically confirmed unresectable stage III or stage IV melanoma not amenable to local therapy and showed disease progression despite having received prior therapies according to the current guidelines. Patients with BRAFV600 mutant tumors had received the recommended first-line or an experimental treatment including BRAF or MEK inhibitor therapy or both. Prior treatment with ipilimumab, if applicable, was considered to have failed when patients had received a minimum of two doses, 3 mg/kg once every 3 weeks, but showed confirmed disease progression within 24 weeks of the last ipilimumab dose. Before administration of anti PD-1, resolution or improvement of ipilimumab-related adverse events to grade 0-1 and prednisone dose 10 mg/day or less was demanded for at least 2 weeks before the first dose of study drug. Eligible patients had Eastern Cooperative Oncology Group (ECOG) performance status 0 or 1; measurable disease per Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1); and values within the prespecified range for absolute neutrophil count (≥1500 cells per mL), platelets (≥100 000 cells per mL), haemoglobin (≥90 g/L), serum creatinine (≥1.5 upper limit of normal [ULN]), serum total bilirubin (≤1.5 ULN or direct bilirubin ≤ULN for patients with total bilirubin concentrations >1.5 ULN), aspartate and alanine aminotransferases (≤2.5 ULN or ≤5 ULN for patients with liver metastases), international normalised ratio or prothrombin time (≤1.5 ULN if not using anticoagulants), and activated partial thromboplastin time (≤1.5 ULN if not using anticoagulants). Patients had a washout period of at least 4 weeks between the last dose of the most recent therapy and the first dose of pembrolizumab or nivolumab.

Analysis of hGDF-15 Serum Levels by Enzyme-Linked Immunosorbent Assay (ELISA)

Human GDF-15 serum levels were measured by Enzyme-Linked Immunosorbent Assay (ELISA).

Buffers and Reagents:

-   -   Buffered blocking solution: 1% BSA (fraction V pH 7.0, PAA,         Pasching, Austria) in PBS     -   Wash solution: PBS-Tween (0.05%)     -   Standard: human GDF-15 (stock concentration 120 μg/ml, from R&D         Systems)     -   Capture antibody: Human GDF-15 MAb (Clone 147627) from R&D         Systems, Mouse IgG2B (catalog #MAB957, from R&D Systems, stock         concentration 360 μg/ml)     -   Detection antibody: Human GDF-15 Biotinylated Affinity Purified         PAb, Goat IgG (catalog #BAF940, from R&D Systems, stock         concentration 9 μl/ml)     -   Streptavidin-HRP (Catalog #DY998, from R&D Systems)     -   Substrate solution: 10 ml 0.1 M NaOAc pH6.0+100 μl TMB+2 μl H₂O₂     -   Stop solution: 1 M H₂SO₄

Analysis Procedure: 1. Plate Preparation:

-   -   e. The capture antibody was diluted to the working concentration         of 2 μg/ml in PBS. A 96-well microplate (Nunc maxisorp®) was         immediately coated with 50 μl per well of the diluted capture         antibody excluding the outer rows (A and H). Rows A and H were         filled with buffer to prevent evaporation of the samples during         the experiment. The plate was gently tapped to ensure that the         bottom of each well was thoroughly covered. The plate was placed         in a humid chamber and incubated overnight at room temperature         (RT).     -   f. Each well was aspirated and washed three times with PBS-Tween         (0.05%).     -   g. 150 μl of blocking solution was added to each well, followed         by incubation at RT for 1 hour.     -   h. Each well was aspirated and washed three times with PBS-Tween         (0.05%).

2. Assay Procedure:

-   -   d. Standards were prepared. GDF-15 was diluted in buffered         blocking solution to a final concentration of 1 ng/ml (4.17 μl         GDF+496 μl buffered blocking solution). 1:2 serial dilutions         were made.     -   e. Duplicate samples 1:20 (6 μl+114 μl buffered blocking         solution) were prepared.     -   f. 50 μl of diluted samples or standards were added per well,         followed by incubation for 1 hour at RT.

1 2 3 4 5 6 7 8 9 10 11 12 A 0 0 0 0 0 0 0 0 0 0 0 0 B s1  s2  . . . s12 C s1  s2  . . . s12 D s13 s14 . . . s24 E s13 s14 . . . s24 F St and ard dil uti on s G se rial H 0 0 0 0 0 0 0 0 0 0 0 0

-   -   i. Each well was aspirated and washed three times with PBS-Tween         (0.05%).     -   j. The detection antibody was diluted to a final concentration         of 50 ng/ml (56 μl+10 ml blocking buffer). 50 μl of the diluted         detection antibody was added to each well, followed by         incubation for 1 hour at RT.     -   k. Each well was aspirated and washed three times with PBS-Tween         (0.05%).     -   l. Streptavidin-HRP was diluted 1:200 (50 μl+10 ml blocking         buffer). 50 μl of the working dilution of Streptavidin-HRP was         added to each well, followed by incubation for 20 min at RT.     -   m. Each well was aspirated and washed three times with PBS-Tween         (0.05%).     -   n. The substrate solution was prepared. 50 μof substrate         solution was added to each well, followed by incubation for 20         min at RT.     -   o. 50 ∥l of stop solution was added to each well.     -   p. The optical density of each well was determined immediately,         using a microplate reader set to 450 nm.

3. Calculation of GDF-15 Serum Titer:

-   -   d. Each sample/GDF-15 standard dilution was applied in         duplicate. To determine GDF-15 titer, the average of the         duplicates was calculated and the background (sample without         GDF-15) subtracted.     -   e. To create a standard curve, values from the linear range were         plotted on an X-Y-diagram (X axis: GDF-15 concentration, Y axis:         OD450), and a linear curve fit was applied. GDF-15 serum titer         of the test samples was calculated by interpolating from the         OD450 values of the standard dilutions with known concentration.     -   f. To calculate the final GDF-15 concentration of the samples,         the distinct dilution factor was considered. Samples yielding OD         values below or above the standard range were re-analyzed at         appropriate dilutions.

Comparison of hGDF-15 Serum Levels with Patient Data

Next, the measured hGDF-15 serum levels were compared with patient response data obtained from the study.

Statistical Correlation of hGDF-15 Serum Levels with Patient Data Data:

The data analysis was based on a data file containing data from samples from 99 patients containing the columns (variables) Sample designation, GDF-15 (ng/ml), days (to death or censoring), and Ongoing (an index variable for ongoing life).

Outcome Variables (Endpoints):

-   -   a. Overall survival (time to death). This endpoint is composed         of the event indicator for death (1=dead/0=alive), which was         derived from the data file, and the time to death or censoring         (last time the patient was known to be alive), corresponding to         the variable “days”.

Response to treatment, e.g. whether a patient was a responder or not (coded as 1=r)

Data Analysis:

Follow-up time for survival analysis was defined from the date of blood sampling to the last follow-up (i.e. the last information obtained from the patient) or death. All blood samples were taken within days prior to the treatment with the anti-PD1 antibody. For the analysis of OS, patients who were alive at the last follow-up were censored while patients who had died were considered an “event”. Cumulative survival probabilities according to Kaplan-Meier were calculated together with 95% confidence intervals (CIs) and compared using two-sided log-rank test statistics. p-values for overall survival were calculated by two-sided log rank statistics. One model was fitted with a grouping variable based on GDF-15 as categorical predictor (groups were: <1.5 ng/ml (n=62), ng/ml (n=37) or GDF-15^(low) (n=49), GDF-15^(high) (n=50), based on a median split). The resulting Kaplan-Meier curves are shown in FIGS. 11 and 12 where censoring is indicated by vertical lines. Additionally, the following tables contain a summary of the cases (Table 9), patient survival data for patient groups having GDF-15 levels of <1.5 ng/ml and ng/ml (Tables 10 and 11) and total statistical comparisons of the patient groups having GDF-15 levels of <1.5 ng/ml and ng/ml (Table 12).

TABLE 9 Summary of Cases Number of Censored Number events H* % Survival GDF-15 < 1.5 ng/ml 62 11 51 82.3% GDF-15 ≥ 1..5 ng/ml 37 18 19 51.4% Total 99 29 70 70.7% *H = event-free

TABLE 10 Mean and Median for Survival (number of days of survival) Mean^(a) 95%-Confidence interval Median Esti- Standard lower upper Esti- Standard mate error limit limit mate error <1.5 701.928 44.172 615.350 788.506 n/d. n/d. ng/ml ≥1.5 381.683 48.882 285.875 477.491 309.000 127.570 ng/ml Total 569.056 44.477 481.882 656.231 n/d. n/d. ^(a)After censoring the estimate is limited to the longest known survival. n/d: No median survival data could be calculated due to the presence of >50% survivors in the group.

TABLE 11 Mean and Median for Duration of Survival (number of days of survival) Median^(a) 95%-confidence interval lower limit upper limit <1.5 ng/ml n/d. n/d. ≥1.5 ng/ml 58.963 559.037 Total n/d. n/d. ^(a)After censoring the estimate is limited to the longest known survival. n/d: No median survival data could be calculated due to the presence of >50% survivors in the group.

TABLE 12 Total comparisons Chi-square df* Significance Log Rank (Mantel-Cox) 8.129 1 .004 *df = degrees of freedom Test on equal distribution of survival for different levels of GDF-15 (<1.5 ng/ml, ≥1.5 ng/ml)

Results and Conclusions

The above statistical results of this Example further confirmed the results of Example 1. For instance, it was confirmed that the probability of a positive clinical outcome of the treatment, as indicated by the survival of the patients, significantly decreases with increasing hGDF-15 levels in the patient sera. For example, Table 12 shows that the survival between the two patient groups having GDF-15 levels of <1.5 ng/ml and ≥1.5 ng/ml, respectively, was significantly different, as evidenced by a significance level of 0.004. Similarly, Table 9 demonstrates that a higher percentage of patients (82.3%) survived in the group having GDF-15 levels of <1.5 ng/ml, and Tables 10 and 11 and FIGS. 11 and 12 demonstrate that for patients having GDF-15 levels of <1.5 ng/ml, survival times were remarkably longer than in patients having GDF-15 levels of ≥1.5 ng/ml.

Thus, the results of this Example further confirm that there is a strong inverse correlation between the serum levels of hGDF-15 and the probability of a positive clinical outcome of e.g. anti PD-1 based immunotherapy in the patients, including patient response and patient survival. Thus, according to the invention, levels of hGDF-15 in blood samples from patients can advantageously be used to predict the probability of a response of patients to a treatment with immune checkpoint blockers like anti PD-1.

Example 5: In Human Non-Small Cell Lung Cancer (NSCLC) Patients Treated with an Anti-PD1 Antibody, Median hGDF-15 Serum Levels in Patients With Progressive Disease are Higher Than in Patients Showing a Partial Response

This Example was performed in order to further validate the results obtained in the study of Example 1, e.g. the finding that hGDF-15 allows to predict the patients' response to immune checkpoint blockers, in an additional independent study in a different solid cancer.

Patients

NSCLC patients were treated with anti-PD1 antibodies in accordance with the approved drug label of the anti-PD1 antibodies. The patients included patients who were pre-treated with other cancer therapies. Due to the fact that a complete response is rarely observed in NSCLC patients, the patient group included patients showing progressive disease and showing a partial response upon PD-1 treatment, but no patients showing a complete response upon PD-1 treatment.

Serum Samples

Serum samples were taken from the patients prior to the treatment with the anti-PD1 antibodies.

Analysis of hGDF-15 Serum Levels by Enzyme-Linked Immunosorbent Assay (ELISA)

hGDF-15 serum levels in the serum samples were analyzed by Enzyme-Linked Immunosorbent Assay (ELISA), as described in Example 1.

Results

hGDF-15 serum levels from 5 patients showing a partial response upon treatment with anti-PD-1, and from 5 patients showing progressive disease upon treatment with anti-PD-1, were obtained. Notably, the median hGDF-15 serum level in the patients showing a partial response was 0.55 ng/ml, whereas the median hGDF-15 serum level in the patients showing progressive disease was 1.56 ng/ml. Thus, the median hGDF-15 serum level in the patients showing a progressive disease was about 2.8-fold higher than in the patients showing a partial response.

Conclusions

The results of this Example further confirm that hGDF-15 levels negatively correlate with the patients' response to immune checkpoint blockers. Thus, according to the invention, levels of hGDF-15 in blood samples from patients can advantageously be used to predict the probability of a response of patients to a treatment with immune checkpoint blockers like anti PD-1. Such predictions can not only be made for melanoma, but also for, but also in lung cancers such as NSCLC and in all of the other solid cancers referred to herein.

Example 6: hGDF-15 Serum Levels do not Significantly Correlate with the Mutational Burden of the Tumors

The mutational burden is a known positive prognostic factor for a response of cancer patients to immune checkpoint blockers. Generally, cancer cells harbor genomic mutations which give rise to cancer cell antigens that are specific to the cancer cells and different from the antigens of non-cancerous cells. A high mutational burden leads to a high number of such cancer cell-specific antigens. In cancers harboring such a high number of cancer cell-specific antigens, the stimulation of the immune response by immune checkpoint blockers is considered to be particularly effective, because more cancer cell-specific antigens are available as target antigens for the immune response.

In order to further confirm that hGDF-15 is not merely a surrogate marker for the mutational burden of the tumors, and in order to further confirm that a treatment with hGDF-15 inhibitors acts via a mechanism that is independent from the mutational burden of the tumors, hGDF-15 mRNA levels in cancer samples from cancer patients were plotted against the number of somatic mutations which were identified in the cancers. The somatic mutations were determined by use of exome sequencing. The data were analyzed by using the UZH webtool from the University Hospital Zurich (Cheng P F et al.: Data mining The Cancer Genome Atlas in the era of precision cancer medicine. Swiss Med Wkly. 2015 Sep. 16; 145:w14183.) The results are shown in FIG. 13. FIG. 13A shows a plot for cancer patient data obtained from the Cancer Genome Atlas (TGCA) considering only patients with high-grade malignant melanoma (the Cancer Genome Atlas is described in the reference of Cheng P F et al.: Data mining The Cancer Genome Atlas in the era of precision cancer medicine. Swiss Med Wkly. 2015 Sep. 16; 145:w14183.). GDF-15 expression was evaluated by normalization using the RSEM (“RNA Seq by expectation maximization”) software package (Li B and Dewey C N: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011 Aug. 4; 12:323. doi: 10.1186/1471-2105-12-323.). FIG. 13B shows a plot for cancer patient data from 40 additional metastatic malignant melanoma patients from the University Hospital Zurich, which were separately analyzed.

Notably, both FIGS. 13A and 13B show a p value of 0.5, indicating that there is no significant correlation between the mutational burden in the cancers and the levels of hGDF-15. These results further confirm that hGDF-15 is not merely a surrogate marker for the mutational burden of the tumors, and that hGDF-15 levels allow to predict the patients' responses to immune checkpoint blockers in a manner which is independent from the mutational burden of the tumors.

Example 7: CD8⁺ T-Cell Infiltration in Wild-Type Tumors or Human GDF-15 (Over)Expressing Tumors

In a pilot study using either wild-type or human GDF-15 (over)expressing MC38 colon cancer cells implanted in the right flank of immunocompetent syngeneic mice C57BL/6, GDF-15 overexpression was associated with reduced immune cell infiltration. Immunocytochemistry pictures for CD8a in mice sacrificed after 29 days harboring wild-type tumors or tumors overexpressing transgenic (tg) hGDF15 are shown in FIG. 14. As can be seen from the Figure, the wild-type tumors contained more CD8a-positive cells than the tumors overexpressing transgenic (tg) hGDF15.

These results further support the finding that according to the present invention, hGDF-15 decreases the percentage of CD8⁺ T cells in solid cancers. Thus, according to the invention, a preferred but not limiting explanation for the inverse correlation of hGDF-15 levels and a favorable clinical outcome (e.g. patient survival or the presence of a treatment response) is that hGDF-15 decreases the percentage of CD8⁺ T lymphocytes in solid tumors including tumor metastases, thereby decreasing the probability of a favorable clinical outcome (e.g. patient survival or the presence of a treatment response). Since this correlation is observed across various solid cancer entities, the present invention is not limited to particular solid cancers such as melanoma.

INDUSTRIAL APPLICABILITY

The apparatuses and the kits according to the present invention may be industrially manufactured and sold as products for the claimed prediction methods, in accordance with known standards for the manufacture of diagnostic products. Accordingly, the present invention is industrially applicable.

REFERENCES

-   Arbabi Ghahroudi M et al.: “Selection and identification of single     domain antibody fragments from camel heavy-chain antibodies.” FEBS     Lett. 1997 Sep. 15; 414(3):521-6. -   Ausubel et al.: “Current Protocols in Molecular Biology.” Greene     Publishing Associates and Wiley Interscience; New York 1992. -   Bauskin A R et al.: “The propeptide mediates formation of stromal     stores of PROMIC-1: role in determining prostate cancer outcome.”     Cancer Res. 2005 Mar. 15; 65(6):2330-6. -   Brown D A et al.: “Macrophage inhibitory cytokine 1: a new     prognostic marker in prostate cancer.” Clin Cancer Res. 2009 Nov. 1;     15(21):6658-64. -   Cheng P F et al.: Data mining The Cancer Genome Atlas in the era of     precision cancer medicine. Swiss Med Wkly. 2015 Sep. 16; 145:w14183. -   Chothia C et al.: Conformations of immunoglobulin hypervariable     regions. Nature. 1989 Dec. 21-28; 342(6252):877-83. -   Clackson T et al.: “Making antibody fragments using phage display     libraries.” Nature. 1991 Aug. 15; 352(6336):624-8. -   Cully M: “Combinations with checkpoint inhibitors at wavefront of     cancer immunotherapy.” Nat Rev Drug Discov. 2015 June; 14(6):374-5. -   Eisenhauer et al.: New response evaluation criteria in solid     tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer. 45,     No. 2, January 2009, pp 228-47. -   Giudicelli V et al.: IMGTN-QUEST, an integrated software program for     immunoglobulin and T cell receptor V-J and V-D-J rearrangement     analysis. Nucleic Acids Res. 2004 Jul. 1; 32(Web Server     issue):W435-40. -   Gouttefangeas C et al.: “Flow Cytometry in Cancer Immunotherapy:     Applications, Quality Assurance and Future.” (2015) In: Cancer     Immunology: Translational Medicine from Bench to Bedside (N. Rezaei     editor). Springer. Chapter 25: pages 471-486; and the methods     according to -   Harlow and Lane: “Antibodies: A Laboratory Manual” Cold Spring     Harbor Laboratory Press, Cold Spring Harbor, New York 1988. -   Holliger P et al.: “”Diabodies“: small bivalent and bispecific     antibody fragments.” Proc Natl Acad Sci U S A. 1993 Jul. 15;     90(14):6444-8. -   Holt L J et al.: “Domain antibodies: proteins for therapy.” Trends     Biotechnol. 2003 November; 21(11):484-90. -   Huang C Y et al.: “Molecular alterations in prostate carcinomas that     associate with in vivo exposure to chemotherapy: identification of a     cytoprotective mechanism involving growth differentiation factor     15.” Clin Cancer Res. 2007 Oct. 1; 13(19):5825-33. -   Johnen H et al.: “Tumor-induced anorexia and weight loss are     mediated by the TGF-beta superfamily cytokine MIC-1.” Nat Med. 2007     November; 13(11):1333-40. -   Jones P T et al.: “Replacing the complementarity-determining regions     in a human antibody with those from a mouse.” Nature. 1986 May     29-Jun. 4; 321(6069):522-5. -   Kabat et al.: Sequences of proteins of immunological interest, U.S.     Dept. of Health and Human Services, Public Health Service, National     Institutes of Health, Bethesda, Md. 1983. -   Kanasty Ret al., “Delivery materials for siRNA therapeutics.”, Nat     Mater. 2013 November; 12(11):967-77. -   Kohler G and Milstein C: “Continuous cultures of fused cells     secreting antibody of predefined specificity.” Nature. 1975 Aug. 7;     256(5517):495-7. -   Lasithiotakis, K G et al., Cancer/107/1331-9. 2006. -   Li B and Dewey C N: RSEM: accurate transcript quantification from     RNA-Seq data with or without a reference genome. BMC Bioinformatics.     2011 Aug. 4; 12:323. doi: 10.1186/1471-2105-12-323. -   Marks J D et al.: “By-passing immunization. Human antibodies from     V-gene libraries displayed on phage.” J Mol Biol. 1991 Dec. 5;     222(3):581-97. -   Mimeault M and Batra S K: “Divergent molecular mechanisms underlying     the pleiotropic functions of macrophage inhibitory cytokine-1 in     cancer.” J Cell Physiol. 2010 September; 224(3):626-35. -   Motzer R J et al., Nivolumab versus Everolimus in Advanced     Renal-Cell Carcinoma. N Engl J Med. 2015 Sep. 25. -   Paul, W. E. (Ed.).: “Fundamental Immunology” 2nd Ed. Raven Press,     Ltd., New York 1989. -   Remington's Pharmaceutical Sciences, Ed. AR Gennaro, 20th edition,     2000, Williams & Wilkins, PA, USA. -   R Core Team (2014). R: A language and environment for statistical     computing. R Foundation for Statistical Computing, Vienna, Austria.     URL http://www.R-project.org/. -   Riechmann Let al.: “Reshaping human antibodies for therapy.” Nature.     1988 Mar. 24; 332(6162):323-7. -   Rizvi N A et al.: “Cancer immunology. Mutational landscape     determines sensitivity to PD-1 blockade in non-small cell lung     cancer.” Science 2015 Apr. 3; 348(6230):124-8. -   Roth P et al.: “GDF-15 contributes to proliferation and immune     escape of malignant gliomas.” Clin Cancer Res. 2010 Aug. 1;     16(15):3851-9. -   Saerens D et al.: “Single-domain antibodies as building blocks for     novel therapeutics.” Curr Opin Pharmacol. 2008 October; 8(5):600-8.     Epub 2008 Aug. 22. -   Sambrook et al.: “Molecular Cloning: A Laboratory Manual.”, 2nd Ed.,     Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York     1989. -   Siegel D L: “Recombinant monoclonal antibody technology.” Transfus     Clin Biol. 2002 January; 9(1):15-22. -   Stefanescu R. et al., Eur. J. Mass Spectrom. 13, 69-75 (2007) -   Suckau et al. Proc Natl Acad Sci U S A. 1990 December; 87(24):     9848-9852. -   Taube et al., “Association of PD-1, PD-1 ligands, and other features     of the tumor immune microenvironment with response to anti-PD-1     therapy.”, Clin Cancer Res. 2014 Oct. 1; 20(19):5064-74. -   K K Tsai et al., JCO 33, 2015 (suppl. abstr. 9031). -   Tumeh et al., PD-1 blockade induces responses by inhibiting adaptive     immune resistance. Nature. 2014 Nov. 27; 515(7528):568-71. -   Van der Burg S H, et al.: “Immunoguiding, the final frontier in the     immunotherapy of cancer.” (2014) In Cancer Immunotherapy meets     oncology (CM Britten, S Kreiter, M. Diken & HG Rammensee eds).     Springer International Publishing Switzerland p37-51 ISBN:     978-3-319-05103-1. -   Yadav M et al.: Predicting immunogenic tumour mutations by combining     mass spectrometry and exome sequencing. Nature. 2014 Nov. 27;     515(7528):572-6. -   Wallentin Let al.: “GDF-15 for prognostication of cardiovascular and     cancer morbidity and mortality in men.” PLoS One. 2013 Dec. 2;     8(12):e78797. -   Wang A et al.: “The prognostic value of PD-L1 expression for     non-small cell lung cancer patients: a meta-analysis.” Eur J Surg     Oncol. 2015 April; 41(4):450-6. -   Weinberg R. et al.: The Biology of Cancer. Garland Science: New York     2006.850p. -   WO 2005/099746 -   WO 2009/021293 -   WO 2014/049087 -   WO 2014/100689 -   PCT/EP2015/056654 -   Zhang, J., Yao, Y.-H., Li, B.-G., Yang, Q., Zhang, P.-Y., and Wang,     H.-T. (2015). Prognostic value of pretreatment serum lactate     dehydrogenase level in patients with solid tumors: a systematic     review and meta-analysis. Scientific Reports 5, 9800). -   Zhou et al. Growth differentiation factor-15 suppresses maturation     and function of dendritic cells and inhibits tumor-specific immune     response. PLoS One. 2013 Nov. 13; 8(11):e78618. 

1-37. (canceled)
 38. A method for treating a solid cancer in a patient in need thereof, the method comprising the steps of: (a) selecting a patient that has a blood level of hGDF-15 that is more than 1.2 ng/ml; and (b) administering an immune checkpoint blocker to the patient.
 39. The method of claim 38, wherein the blood level of hGDF-15 is between 1.2 ng/ml and 8.0 ng/ml.
 40. The method of claim 38, wherein the blood level of hGDF-15 is between 3.0 ng/ml and 4.0 ng/ml.
 41. The method of claim 38, wherein the blood level of hGDF-15 is around 3.4 ng/ml.
 42. The method of claim 38, wherein the patient is receiving or has received treatment for a solid cancer.
 43. The method of claim 38, wherein the cancer is selected from the group consisting of melanoma, colorectal cancer, prostate cancer, head and neck cancer, urothelial cancer, stomach cancer, pancreatic cancer, liver cancer, testis cancer, ovarian cancer, endometrial cancer, cervical cancer, brain cancer, breast cancer, gastric cancer, renal cell carcinoma, Ewing's sarcoma, non-small cell lung cancer, and small cell lung cancer.
 44. The method of claim 38, wherein the cancer is stage III or stage IV melanoma.
 45. The method of claim 38, wherein the cancer is unresectable stage III melanoma or stage IV melanoma not amenable to local therapy.
 46. The method of claim 38, wherein the immune checkpoint blocker is selected from the group consisting of an anti-PD-1 antibody, an anti-CTLA4 antibody, an anti-PD-L1 antibody, an anti-LAG3 antibody, an anti-B7H3 antibody, an anti-TIM3 antibody, an anti-VISTA antibody, an anti-TIGIT antibody, an anti-KIR antibody, an anti-CD27 antibody, and an anti-CD137 antibody.
 47. The method of claim 46, wherein the immune checkpoint blocker is an anti-PD-1 antibody selected from the group consisting of nivolumab, pembrolizumab, pidilizumab, and AMP-224.
 48. The method of claim 46, wherein the immune checkpoint blocker is ipilimumab.
 49. The method of claim 46, wherein the immune checkpoint blocker is an anti-PD-L1 antibody selected from the group consisting of BMS-936559, MPDL3280A, MEDI4736, and MSB0010718C.
 50. The method of claim 38, wherein the immune checkpoint blocker is an IDO inhibitor. 